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images</title> <meta name="description" content="Search results for: satellite images"> <meta name="keywords" content="satellite images"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="satellite images"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 2905</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: satellite images</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2905</span> Timing Equation for Capturing Satellite Thermal Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Toufic%20Abd%20El-Latif%20Sadek">Toufic Abd El-Latif Sadek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Asphalt object represents the asphalted areas, like roads. The best original data of thermal images occurred at a specific time during the days of the year, by preventing the gaps in times which give the close and same brightness from different objects, using seven sample objects, asphalt, concrete, metal, rock, dry soil, vegetation, and water. It has been found in this study a general timing equation for capturing satellite thermal images at different locations, depends on a fixed time the sunrise and sunset; Capture Time= Tcap =(TM*TSR) ±TS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asphalt" title="asphalt">asphalt</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite" title=" satellite"> satellite</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20images" title=" thermal images"> thermal images</a>, <a href="https://publications.waset.org/abstracts/search?q=timing%20equation" title=" timing equation"> timing equation</a> </p> <a href="https://publications.waset.org/abstracts/51769/timing-equation-for-capturing-satellite-thermal-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51769.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2904</span> Using Satellite Images Datasets for Road Intersection Detection in Route Planning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatma%20El-Zahraa%20El-Taher">Fatma El-Zahraa El-Taher</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayman%20Taha"> Ayman Taha</a>, <a href="https://publications.waset.org/abstracts/search?q=Jane%20Courtney"> Jane Courtney</a>, <a href="https://publications.waset.org/abstracts/search?q=Susan%20Mckeever"> Susan Mckeever</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions, is critical to decisions such as crossing roads or selecting the safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer the state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset is examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of the detection of intersections in satellite images is evaluated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=satellite%20images" title="satellite images">satellite images</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing%20images" title=" remote sensing images"> remote sensing images</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20acquisition" title=" data acquisition"> data acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20vehicles" title=" autonomous vehicles"> autonomous vehicles</a> </p> <a href="https://publications.waset.org/abstracts/145141/using-satellite-images-datasets-for-road-intersection-detection-in-route-planning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145141.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">144</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2903</span> Best Timing for Capturing Satellite Thermal Images, Asphalt, and Concrete Objects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Toufic%20Abd%20El-Latif%20Sadek">Toufic Abd El-Latif Sadek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The asphalt object represents the asphalted areas like roads, and the concrete object represents the concrete areas like concrete buildings. The efficient extraction of asphalt and concrete objects from one satellite thermal image occurred at a specific time, by preventing the gaps in times which give the close and same brightness values between asphalt and concrete, and among other objects. So that to achieve efficient extraction and then better analysis. Seven sample objects were used un this study, asphalt, concrete, metal, rock, dry soil, vegetation, and water. It has been found that, the best timing for capturing satellite thermal images to extract the two objects asphalt and concrete from one satellite thermal image, saving time and money, occurred at a specific time in different months. A table is deduced shows the optimal timing for capturing satellite thermal images to extract effectively these two objects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asphalt" title="asphalt">asphalt</a>, <a href="https://publications.waset.org/abstracts/search?q=concrete" title=" concrete"> concrete</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20thermal%20images" title=" satellite thermal images"> satellite thermal images</a>, <a href="https://publications.waset.org/abstracts/search?q=timing" title=" timing"> timing</a> </p> <a href="https://publications.waset.org/abstracts/51827/best-timing-for-capturing-satellite-thermal-images-asphalt-and-concrete-objects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51827.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">322</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2902</span> Remote Sensing through Deep Neural Networks for Satellite Image Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Teja%20Sai%20Puligadda">Teja Sai Puligadda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Satellite images in detail can serve an important role in the geographic study. Quantitative and qualitative information provided by the satellite and remote sensing images minimizes the complexity of work and time. Data/images are captured at regular intervals by satellite remote sensing systems, and the amount of data collected is often enormous, and it expands rapidly as technology develops. Interpreting remote sensing images, geographic data mining, and researching distinct vegetation types such as agricultural and forests are all part of satellite image categorization. One of the biggest challenge data scientists faces while classifying satellite images is finding the best suitable classification algorithms based on the available that could able to classify images with utmost accuracy. In order to categorize satellite images, which is difficult due to the sheer volume of data, many academics are turning to deep learning machine algorithms. As, the CNN algorithm gives high accuracy in image recognition problems and automatically detects the important features without any human supervision and the ANN algorithm stores information on the entire network (Abhishek Gupta., 2020), these two deep learning algorithms have been used for satellite image classification. This project focuses on remote sensing through Deep Neural Networks i.e., ANN and CNN with Deep Sat (SAT-4) Airborne dataset for classifying images. Thus, in this project of classifying satellite images, the algorithms ANN and CNN are implemented, evaluated & compared and the performance is analyzed through evaluation metrics such as Accuracy and Loss. Additionally, the Neural Network algorithm which gives the lowest bias and lowest variance in solving multi-class satellite image classification is analyzed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=loss" title=" loss"> loss</a> </p> <a href="https://publications.waset.org/abstracts/146723/remote-sensing-through-deep-neural-networks-for-satellite-image-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146723.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">159</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2901</span> Reinforcement Learning for Classification of Low-Resolution Satellite Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khadija%20Bouzaachane">Khadija Bouzaachane</a>, <a href="https://publications.waset.org/abstracts/search?q=El%20Mahdi%20El%20Guarmah"> El Mahdi El Guarmah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The classification of low-resolution satellite images has been a worthwhile and fertile field that attracts plenty of researchers due to its importance in monitoring geographical areas. It could be used for several purposes such as disaster management, military surveillance, agricultural monitoring. The main objective of this work is to classify efficiently and accurately low-resolution satellite images by using novel technics of deep learning and reinforcement learning. The images include roads, residential areas, industrial areas, rivers, sea lakes, and vegetation. To achieve that goal, we carried out experiments on the sentinel-2 images considering both high accuracy and efficiency classification. Our proposed model achieved a 91% accuracy on the testing dataset besides a good classification for land cover. Focus on the parameter precision; we have obtained 93% for the river, 92% for residential, 97% for residential, 96% for the forest, 87% for annual crop, 84% for herbaceous vegetation, 85% for pasture, 78% highway and 100% for Sea Lake. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title=" reinforcement learning"> reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20imagery" title=" satellite imagery"> satellite imagery</a> </p> <a href="https://publications.waset.org/abstracts/141097/reinforcement-learning-for-classification-of-low-resolution-satellite-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141097.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">213</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2900</span> Mutual Information Based Image Registration of Satellite Images Using PSO-GA Hybrid Algorithm </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dipti%20Patra">Dipti Patra</a>, <a href="https://publications.waset.org/abstracts/search?q=Guguloth%20Uma"> Guguloth Uma</a>, <a href="https://publications.waset.org/abstracts/search?q=Smita%20Pradhan"> Smita Pradhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Registration is a fundamental task in image processing. It is used to transform different sets of data into one coordinate system, where data are acquired from different times, different viewing angles, and/or different sensors. The registration geometrically aligns two images (the reference and target images). Registration techniques are used in satellite images and it is important in order to be able to compare or integrate the data obtained from these different measurements. In this work, mutual information is considered as a similarity metric for registration of satellite images. The transformation is assumed to be a rigid transformation. An attempt has been made here to optimize the transformation function. The proposed image registration technique hybrid PSO-GA incorporates the notion of Particle Swarm Optimization and Genetic Algorithm and is used for finding the best optimum values of transformation parameters. The performance comparision obtained with the experiments on satellite images found that the proposed hybrid PSO-GA algorithm outperforms the other algorithms in terms of mutual information and registration accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20registration" title="image registration">image registration</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20PSO-GA%20algorithm%20and%20mutual%20information" title=" hybrid PSO-GA algorithm and mutual information"> hybrid PSO-GA algorithm and mutual information</a> </p> <a href="https://publications.waset.org/abstracts/9683/mutual-information-based-image-registration-of-satellite-images-using-pso-ga-hybrid-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9683.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">407</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2899</span> Towards Update a Road Map Solution: Use of Information Obtained by the Extraction of Road Network and Its Nodes from a Satellite Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Z.%20Nougrara">Z. Nougrara</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Meunier"> J. Meunier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a new approach for extracting roads, there road network and its nodes from satellite image representing regions in Algeria. Our approach is related to our previous research work. It is founded on the information theory and the mathematical morphology. We therefore have to define objects as sets of pixels and to study the shape of these objects and the relations that exist between them. The main interest of this study is to solve the problem of the automatic mapping from satellite images. This study is thus applied for that the geographical representation of the images is as near as possible to the reality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nodes" title="nodes">nodes</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20network" title=" road network"> road network</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20image" title=" satellite image"> satellite image</a>, <a href="https://publications.waset.org/abstracts/search?q=updating%20a%20road%20map" title=" updating a road map"> updating a road map</a> </p> <a href="https://publications.waset.org/abstracts/25331/towards-update-a-road-map-solution-use-of-information-obtained-by-the-extraction-of-road-network-and-its-nodes-from-a-satellite-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25331.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">425</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2898</span> Water Body Detection and Estimation from Landsat Satellite Images Using Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Devaki">M. Devaki</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20B.%20Jayanthi"> K. B. Jayanthi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The identification of water bodies from satellite images has recently received a great deal of attention. Different methods have been developed to distinguish water bodies from various satellite images that vary in terms of time and space. Urban water identification issues body manifests in numerous applications with a great deal of certainty. There has been a sharp rise in the usage of satellite images to map natural resources, including urban water bodies and forests, during the past several years. This is because water and forest resources depend on each other so heavily that ongoing monitoring of both is essential to their sustainable management. The relevant elements from satellite pictures have been chosen using a variety of techniques, including machine learning. Then, a convolution neural network (CNN) architecture is created that can identify a superpixel as either one of two classes, one that includes water or doesn't from input data in a complex metropolitan scene. The deep learning technique, CNN, has advanced tremendously in a variety of visual-related tasks. CNN can improve classification performance by reducing the spectral-spatial regularities of the input data and extracting deep features hierarchically from raw pictures. Calculate the water body using the satellite image's resolution. Experimental results demonstrate that the suggested method outperformed conventional approaches in terms of water extraction accuracy from remote-sensing images, with an average overall accuracy of 97%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=water%20body" title="water body">water body</a>, <a href="https://publications.waset.org/abstracts/search?q=Deep%20learning" title=" Deep learning"> Deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20images" title=" satellite images"> satellite images</a>, <a href="https://publications.waset.org/abstracts/search?q=convolution%20neural%20network" title=" convolution neural network"> convolution neural network</a> </p> <a href="https://publications.waset.org/abstracts/162827/water-body-detection-and-estimation-from-landsat-satellite-images-using-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162827.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">89</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2897</span> Selection of Appropriate Classification Technique for Lithological Mapping of Gali Jagir Area, Pakistan </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khunsa%20Fatima">Khunsa Fatima</a>, <a href="https://publications.waset.org/abstracts/search?q=Umar%20K.%20Khattak"> Umar K. Khattak</a>, <a href="https://publications.waset.org/abstracts/search?q=Allah%20Bakhsh%20Kausar"> Allah Bakhsh Kausar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Satellite images interpretation and analysis assist geologists by providing valuable information about geology and minerals of an area to be surveyed. A test site in Fatejang of district Attock has been studied using Landsat ETM+ and ASTER satellite images for lithological mapping. Five different supervised image classification techniques namely maximum likelihood, parallelepiped, minimum distance to mean, mahalanobis distance and spectral angle mapper have been performed on both satellite data images to find out the suitable classification technique for lithological mapping in the study area. Results of these five image classification techniques were compared with the geological map produced by Geological Survey of Pakistan. The result of maximum likelihood classification technique applied on ASTER satellite image has the highest correlation of 0.66 with the geological map. Field observations and XRD spectra of field samples also verified the results. A lithological map was then prepared based on the maximum likelihood classification of ASTER satellite image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ASTER" title="ASTER">ASTER</a>, <a href="https://publications.waset.org/abstracts/search?q=Landsat-ETM%2B" title=" Landsat-ETM+"> Landsat-ETM+</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite" title=" satellite"> satellite</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title=" image classification"> image classification</a> </p> <a href="https://publications.waset.org/abstracts/3823/selection-of-appropriate-classification-technique-for-lithological-mapping-of-gali-jagir-area-pakistan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3823.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">394</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2896</span> Source Separation for Global Multispectral Satellite Images Indexing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aymen%20Bouzid">Aymen Bouzid</a>, <a href="https://publications.waset.org/abstracts/search?q=Jihen%20Ben%20Smida"> Jihen Ben Smida</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose to prove the importance of the application of blind source separation methods on remote sensing data in order to index multispectral images. The proposed method starts with Gabor Filtering and the application of a Blind Source Separation to get a more effective representation of the information contained on the observation images. After that, a feature vector is extracted from each image in order to index them. Experimental results show the superior performance of this approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blind%20source%20separation" title="blind source separation">blind source separation</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20based%20image%20retrieval" title=" content based image retrieval"> content based image retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction%20multispectral" title=" feature extraction multispectral"> feature extraction multispectral</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20images" title=" satellite images"> satellite images</a> </p> <a href="https://publications.waset.org/abstracts/28585/source-separation-for-global-multispectral-satellite-images-indexing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28585.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">403</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2895</span> Automatic Change Detection for High-Resolution Satellite Images of Urban and Suburban Areas</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Antigoni%20Panagiotopoulou">Antigoni Panagiotopoulou</a>, <a href="https://publications.waset.org/abstracts/search?q=Lemonia%20Ragia"> Lemonia Ragia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> High-resolution satellite images can provide detailed information about change detection on the earth. In the present work, QuickBird images of spatial resolution 60 cm/pixel and WorldView images of resolution 30 cm/pixel are utilized to perform automatic change detection in urban and suburban areas of Crete, Greece. There is a relative time difference of 13 years among the satellite images. Multiindex scene representation is applied on the images to classify the scene into buildings, vegetation, water and ground. Then, automatic change detection is made possible by pixel-per-pixel comparison of the classified multi-temporal images. The vegetation index and the water index which have been developed in this study prove effective. Furthermore, the proposed change detection approach not only indicates whether changes have taken place or not but also provides specific information relative to the types of changes. Experimentations with other different scenes in the future could help optimize the proposed spectral indices as well as the entire change detection methodology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=change%20detection" title="change detection">change detection</a>, <a href="https://publications.waset.org/abstracts/search?q=multiindex%20scene%20representation" title=" multiindex scene representation"> multiindex scene representation</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20index" title=" spectral index"> spectral index</a>, <a href="https://publications.waset.org/abstracts/search?q=QuickBird" title=" QuickBird"> QuickBird</a>, <a href="https://publications.waset.org/abstracts/search?q=WorldView" title=" WorldView"> WorldView</a> </p> <a href="https://publications.waset.org/abstracts/132460/automatic-change-detection-for-high-resolution-satellite-images-of-urban-and-suburban-areas" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132460.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">136</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2894</span> A Study of ZY3 Satellite Digital Elevation Model Verification and Refinement with Shuttle Radar Topography Mission</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bo%20Wang">Bo Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the first high-resolution civil optical satellite, ZY-3 satellite is able to obtain high-resolution multi-view images with three linear array sensors. The images can be used to generate Digital Elevation Models (DEM) through dense matching of stereo images. However, due to the clouds, forest, water and buildings covered on the images, there are some problems in the dense matching results such as outliers and areas failed to be matched (matching holes). This paper introduced an algorithm to verify the accuracy of DEM that generated by ZY-3 satellite with Shuttle Radar Topography Mission (SRTM). Since the accuracy of SRTM (Internal accuracy: 5 m; External accuracy: 15 m) is relatively uniform in the worldwide, it may be used to improve the accuracy of ZY-3 DEM. Based on the analysis of mass DEM and SRTM data, the processing can be divided into two aspects. The registration of ZY-3 DEM and SRTM can be firstly performed using the conjugate line features and area features matched between these two datasets. Then the ZY-3 DEM can be refined by eliminating the matching outliers and filling the matching holes. The matching outliers can be eliminated based on the statistics on Local Vector Binning (LVB). The matching holes can be filled by the elevation interpolated from SRTM. Some works are also conducted for the accuracy statistics of the ZY-3 DEM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ZY-3%20satellite%20imagery" title="ZY-3 satellite imagery">ZY-3 satellite imagery</a>, <a href="https://publications.waset.org/abstracts/search?q=DEM" title=" DEM"> DEM</a>, <a href="https://publications.waset.org/abstracts/search?q=SRTM" title=" SRTM"> SRTM</a>, <a href="https://publications.waset.org/abstracts/search?q=refinement" title=" refinement"> refinement</a> </p> <a href="https://publications.waset.org/abstracts/76112/a-study-of-zy3-satellite-digital-elevation-model-verification-and-refinement-with-shuttle-radar-topography-mission" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76112.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">342</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2893</span> Use of Satellite Imaging to Understand Earth’s Surface Features: A Roadmap</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sabri%20Serkan%20Gulluoglu">Sabri Serkan Gulluoglu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is possible with Geographic Information Systems (GIS) that the information about all natural and artificial resources on the earth is obtained taking advantage of satellite images are obtained by remote sensing techniques. However, determination of unknown sources, mapping of the distribution and efficient evaluation of resources are defined may not be possible with the original image. For this reasons, some process steps are needed like transformation, pre-processing, image enhancement and classification to provide the most accurate assessment numerically and visually. Many studies which present the phases of obtaining and processing of the satellite images have examined in the literature study. The research showed that the determination of the process steps may be followed at this subject with the existence of a common whole may provide to progress the process rapidly for the necessary and possible studies which will be. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title="remote sensing">remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20imaging" title=" satellite imaging"> satellite imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=gis" title=" gis"> gis</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20science" title=" computer science"> computer science</a>, <a href="https://publications.waset.org/abstracts/search?q=information" title=" information"> information</a> </p> <a href="https://publications.waset.org/abstracts/5599/use-of-satellite-imaging-to-understand-earths-surface-features-a-roadmap" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5599.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">318</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2892</span> Development of Algorithms for the Study of the Image in Digital Form for Satellite Applications: Extraction of a Road Network and Its Nodes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zineb%20Nougrara">Zineb Nougrara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a novel methodology for extracting a road network and its nodes from satellite images of Algeria country. This developed technique is a progress of our previous research works. It is founded on the information theory and the mathematical morphology; the information theory and the mathematical morphology are combined together to extract and link the road segments to form a road network and its nodes. We, therefore, have to define objects as sets of pixels and to study the shape of these objects and the relations that exist between them. In this approach, geometric and radiometric features of roads are integrated by a cost function and a set of selected points of a crossing road. Its performances were tested on satellite images of Algeria country. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=satellite%20image" title="satellite image">satellite image</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20network" title=" road network"> road network</a>, <a href="https://publications.waset.org/abstracts/search?q=nodes" title=" nodes"> nodes</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20analysis%20and%20processing" title=" image analysis and processing"> image analysis and processing</a> </p> <a href="https://publications.waset.org/abstracts/27882/development-of-algorithms-for-the-study-of-the-image-in-digital-form-for-satellite-applications-extraction-of-a-road-network-and-its-nodes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27882.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">274</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2891</span> Urban Growth Analysis Using Multi-Temporal Satellite Images, Non-stationary Decomposition Methods and Stochastic Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Ben%20Abbes">Ali Ben Abbes</a>, <a href="https://publications.waset.org/abstracts/search?q=ImedRiadh%20Farah"> ImedRiadh Farah</a>, <a href="https://publications.waset.org/abstracts/search?q=Vincent%20Barra"> Vincent Barra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Remotely sensed data are a significant source for monitoring and updating databases for land use/cover. Nowadays, changes detection of urban area has been a subject of intensive researches. Timely and accurate data on spatio-temporal changes of urban areas are therefore required. The data extracted from multi-temporal satellite images are usually non-stationary. In fact, the changes evolve in time and space. This paper is an attempt to propose a methodology for changes detection in urban area by combining a non-stationary decomposition method and stochastic modeling. We consider as input of our methodology a sequence of satellite images <em>I<sub>1</sub>, I<sub>2</sub>, … I<sub>n</sub></em> at different periods (<em>t </em>= 1<em>, </em>2<em>, ..., n</em>). Firstly, a preprocessing of multi-temporal satellite images is applied. (e.g. radiometric, atmospheric and geometric). The systematic study of global urban expansion in our methodology can be approached in two ways: The first considers the urban area as one same object as opposed to non-urban areas (e.g. vegetation, bare soil and water). The objective is to extract the urban mask. The second one aims to obtain a more knowledge of urban area, distinguishing different types of tissue within the urban area. In order to validate our approach, we used a database of Tres Cantos-Madrid in Spain, which is derived from Landsat for a period (from January 2004 to July 2013) by collecting two frames per year at a spatial resolution of 25 meters. The obtained results show the effectiveness of our method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-temporal%20satellite%20image" title="multi-temporal satellite image">multi-temporal satellite image</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20growth" title=" urban growth"> urban growth</a>, <a href="https://publications.waset.org/abstracts/search?q=non-stationary" title=" non-stationary"> non-stationary</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20model" title=" stochastic model"> stochastic model</a> </p> <a href="https://publications.waset.org/abstracts/53255/urban-growth-analysis-using-multi-temporal-satellite-images-non-stationary-decomposition-methods-and-stochastic-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53255.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">428</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2890</span> Facility Detection from Image Using Mathematical Morphology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=In-Geun%20Lim">In-Geun Lim</a>, <a href="https://publications.waset.org/abstracts/search?q=Sung-Woong%20Ra"> Sung-Woong Ra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As high resolution satellite images can be used, lots of studies are carried out for exploiting these images in various fields. This paper proposes the method based on mathematical morphology for extracting the ‘horse's hoof shaped object’. This proposed method can make an automatic object detection system to track the meaningful object in a large satellite image rapidly. Mathematical morphology process can apply in binary image, so this method is very simple. Therefore this method can easily extract the ‘horse's hoof shaped object’ from any images which have indistinct edges of the tracking object and have different image qualities depending on filming location, filming time, and filming environment. Using the proposed method by which ‘horse's hoof shaped object’ can be rapidly extracted, the performance of the automatic object detection system can be improved dramatically. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=facility%20detection" title="facility detection">facility detection</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20image" title=" satellite image"> satellite image</a>, <a href="https://publications.waset.org/abstracts/search?q=object" title=" object"> object</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20morphology" title=" mathematical morphology"> mathematical morphology</a> </p> <a href="https://publications.waset.org/abstracts/67611/facility-detection-from-image-using-mathematical-morphology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67611.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">381</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2889</span> Post-Earthquake Road Damage Detection by SVM Classification from Quickbird Satellite Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moein%20Izadi">Moein Izadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Mohammadzadeh"> Ali Mohammadzadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Detection of damaged parts of roads after earthquake is essential for coordinating rescuers. In this study, an approach is presented for the semi-automatic detection of damaged roads in a city using pre-event vector maps and both pre- and post-earthquake QuickBird satellite images. Damage is defined in this study as the debris of damaged buildings adjacent to the roads. Some spectral and texture features are considered for SVM classification step to detect damages. Finally, the proposed method is tested on QuickBird pan-sharpened images from the Bam City earthquake and the results show that an overall accuracy of 81% and a kappa coefficient of 0.71 are achieved for the damage detection. The obtained results indicate the efficiency and accuracy of the proposed approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SVM%20classifier" title="SVM classifier">SVM classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=disaster%20management" title=" disaster management"> disaster management</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20damage%20detection" title=" road damage detection"> road damage detection</a>, <a href="https://publications.waset.org/abstracts/search?q=quickBird%20images" title=" quickBird images"> quickBird images</a> </p> <a href="https://publications.waset.org/abstracts/26389/post-earthquake-road-damage-detection-by-svm-classification-from-quickbird-satellite-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26389.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">623</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2888</span> Optimal and Best Timing for Capturing Satellite Thermal Images of Concrete Object</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Toufic%20Abd%20El-Latif%20Sadek">Toufic Abd El-Latif Sadek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The concrete object represents the concrete areas, like buildings. The best, easy, and efficient extraction of the concrete object from satellite thermal images occurred at specific times during the days of the year, by preventing the gaps in times which give the close and same brightness from different objects. Thus, to achieve the best original data which is the aim of the study and then better extraction of the concrete object and then better analysis. The study was done using seven sample objects, asphalt, concrete, metal, rock, dry soil, vegetation, and water, located at one place carefully investigated in a way that all the objects achieve the homogeneous in acquired data at the same time and same weather conditions. The samples of the objects were on the roof of building at position taking by global positioning system (GPS) which its geographical coordinates is: Latitude= 33 degrees 37 minutes, Longitude= 35 degrees 28 minutes, Height= 600 m. It has been found that the first choice and the best time in February is at 2:00 pm, in March at 4 pm, in April and may at 12 pm, in August at 5:00 pm, in October at 11:00 am. The best time in June and November is at 2:00 pm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=best%20timing" title="best timing">best timing</a>, <a href="https://publications.waset.org/abstracts/search?q=concrete%20areas" title=" concrete areas"> concrete areas</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal" title=" optimal"> optimal</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20thermal%20images" title=" satellite thermal images"> satellite thermal images</a> </p> <a href="https://publications.waset.org/abstracts/51722/optimal-and-best-timing-for-capturing-satellite-thermal-images-of-concrete-object" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51722.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">354</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2887</span> Classification of Land Cover Usage from Satellite Images Using Deep Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shaik%20Ayesha%20Fathima">Shaik Ayesha Fathima</a>, <a href="https://publications.waset.org/abstracts/search?q=Shaik%20Noor%20Jahan"> Shaik Noor Jahan</a>, <a href="https://publications.waset.org/abstracts/search?q=Duvvada%20Rajeswara%20Rao"> Duvvada Rajeswara Rao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Earth's environment and its evolution can be seen through satellite images in near real-time. Through satellite imagery, remote sensing data provide crucial information that can be used for a variety of applications, including image fusion, change detection, land cover classification, agriculture, mining, disaster mitigation, and monitoring climate change. The objective of this project is to propose a method for classifying satellite images according to multiple predefined land cover classes. The proposed approach involves collecting data in image format. The data is then pre-processed using data pre-processing techniques. The processed data is fed into the proposed algorithm and the obtained result is analyzed. Some of the algorithms used in satellite imagery classification are U-Net, Random Forest, Deep Labv3, CNN, ANN, Resnet etc. In this project, we are using the DeepLabv3 (Atrous convolution) algorithm for land cover classification. The dataset used is the deep globe land cover classification dataset. DeepLabv3 is a semantic segmentation system that uses atrous convolution to capture multi-scale context by adopting multiple atrous rates in cascade or in parallel to determine the scale of segments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=area%20calculation" title="area calculation">area calculation</a>, <a href="https://publications.waset.org/abstracts/search?q=atrous%20convolution" title=" atrous convolution"> atrous convolution</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20globe%20land%20cover%20classification" title=" deep globe land cover classification"> deep globe land cover classification</a>, <a href="https://publications.waset.org/abstracts/search?q=deepLabv3" title=" deepLabv3"> deepLabv3</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20cover%20classification" title=" land cover classification"> land cover classification</a>, <a href="https://publications.waset.org/abstracts/search?q=resnet%2050" title=" resnet 50"> resnet 50</a> </p> <a href="https://publications.waset.org/abstracts/147677/classification-of-land-cover-usage-from-satellite-images-using-deep-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147677.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">139</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2886</span> Automatic Extraction of Arbitrarily Shaped Buildings from VHR Satellite Imagery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Evans%20Belly">Evans Belly</a>, <a href="https://publications.waset.org/abstracts/search?q=Imdad%20Rizvi"> Imdad Rizvi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20M.%20Kadam"> M. M. Kadam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Satellite imagery is one of the emerging technologies which are extensively utilized in various applications such as detection/extraction of man-made structures, monitoring of sensitive areas, creating graphic maps etc. The main approach here is the automated detection of buildings from very high resolution (VHR) optical satellite images. Initially, the shadow, the building and the non-building regions (roads, vegetation etc.) are investigated wherein building extraction is mainly focused. Once all the landscape is collected a trimming process is done so as to eliminate the landscapes that may occur due to non-building objects. Finally the label method is used to extract the building regions. The label method may be altered for efficient building extraction. The images used for the analysis are the ones which are extracted from the sensors having resolution less than 1 meter (VHR). This method provides an efficient way to produce good results. The additional overhead of mid processing is eliminated without compromising the quality of the output to ease the processing steps required and time consumed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=building%20detection" title="building detection">building detection</a>, <a href="https://publications.waset.org/abstracts/search?q=shadow%20detection" title=" shadow detection"> shadow detection</a>, <a href="https://publications.waset.org/abstracts/search?q=landscape%20generation" title=" landscape generation"> landscape generation</a>, <a href="https://publications.waset.org/abstracts/search?q=label" title=" label"> label</a>, <a href="https://publications.waset.org/abstracts/search?q=partitioning" title=" partitioning"> partitioning</a>, <a href="https://publications.waset.org/abstracts/search?q=very%20high%20resolution%20%28VHR%29%20satellite%20imagery" title=" very high resolution (VHR) satellite imagery"> very high resolution (VHR) satellite imagery</a> </p> <a href="https://publications.waset.org/abstracts/76690/automatic-extraction-of-arbitrarily-shaped-buildings-from-vhr-satellite-imagery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76690.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">314</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2885</span> Efficient Schemes of Classifiers for Remote Sensing Satellite Imageries of Land Use Pattern Classifications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20S.%20Patil">S. S. Patil</a>, <a href="https://publications.waset.org/abstracts/search?q=Sachidanand%20Kini"> Sachidanand Kini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Classification of land use patterns is compelling in complexity and variability of remote sensing imageries data. An imperative research in remote sensing application exploited to mine some of the significant spatially variable factors as land cover and land use from satellite images for remote arid areas in Karnataka State, India. The diverse classification techniques, unsupervised and supervised consisting of maximum likelihood, Mahalanobis distance, and minimum distance are applied in Bellary District in Karnataka State, India for the classification of the raw satellite images. The accuracy evaluations of results are compared visually with the standard maps with ground-truths. We initiated with the maximum likelihood technique that gave the finest results and both minimum distance and Mahalanobis distance methods over valued agriculture land areas. In meanness of mislaid few irrelevant features due to the low resolution of the satellite images, high-quality accord between parameters extracted automatically from the developed maps and field observations was found. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahalanobis%20distance" title="Mahalanobis distance">Mahalanobis distance</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20distance" title=" minimum distance"> minimum distance</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised" title=" supervised"> supervised</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised" title=" unsupervised"> unsupervised</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20classification%20accuracy" title=" user classification accuracy"> user classification accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=producer%27s%20classification%20accuracy" title=" producer's classification accuracy"> producer's classification accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood" title=" maximum likelihood"> maximum likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=kappa%20coefficient" title=" kappa coefficient"> kappa coefficient</a> </p> <a href="https://publications.waset.org/abstracts/103621/efficient-schemes-of-classifiers-for-remote-sensing-satellite-imageries-of-land-use-pattern-classifications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103621.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">183</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2884</span> High-Accuracy Satellite Image Analysis and Rapid DSM Extraction for Urban Environment Evaluations (Tripoli-Libya)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdunaser%20Abduelmula">Abdunaser Abduelmula</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Luisa%20M.%20Bastos"> Maria Luisa M. Bastos</a>, <a href="https://publications.waset.org/abstracts/search?q=Jos%C3%A9%20A.%20Gon%C3%A7alves"> José A. Gonçalves </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The modeling of the earth's surface and evaluation of urban environment, with 3D models, is an important research topic. New stereo capabilities of high-resolution optical satellites images, such as the tri-stereo mode of Pleiades, combined with new image matching algorithms, are now available and can be applied in urban area analysis. In addition, photogrammetry software packages gained new, more efficient matching algorithms, such as SGM, as well as improved filters to deal with shadow areas, can achieve denser and more precise results. This paper describes a comparison between 3D data extracted from tri-stereo and dual stereo satellite images, combined with pixel based matching and Wallis filter. The aim was to improve the accuracy of 3D models especially in urban areas, in order to assess if satellite images are appropriate for a rapid evaluation of urban environments. The results showed that 3D models achieved by Pleiades tri-stereo outperformed, both in terms of accuracy and detail, the result obtained from a Geo-eye pair. The assessment was made with reference digital surface models derived from high-resolution aerial photography. This could mean that tri-stereo images can be successfully used for the proposed urban change analyses. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20models" title="3D models">3D models</a>, <a href="https://publications.waset.org/abstracts/search?q=environment" title=" environment"> environment</a>, <a href="https://publications.waset.org/abstracts/search?q=matching" title=" matching"> matching</a>, <a href="https://publications.waset.org/abstracts/search?q=pleiades" title=" pleiades"> pleiades</a> </p> <a href="https://publications.waset.org/abstracts/24670/high-accuracy-satellite-image-analysis-and-rapid-dsm-extraction-for-urban-environment-evaluations-tripoli-libya" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24670.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">330</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2883</span> Multi-Temporal Cloud Detection and Removal in Satellite Imagery for Land Resources Investigation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Feng%20Yin">Feng Yin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clouds are inevitable contaminants in optical satellite imagery, and prevent the satellite imaging systems from acquiring clear view of the earth surface. The presence of clouds in satellite imagery bring negative influences for remote sensing land resources investigation. As a consequence, detecting the locations of clouds in satellite imagery is an essential preprocessing step, and further remove the existing clouds is crucial for the application of imagery. In this paper, a multi-temporal based satellite imagery cloud detection and removal method is proposed, which will be used for large-scale land resource investigation. The proposed method is mainly composed of four steps. First, cloud masks are generated for cloud contaminated images by single temporal cloud detection based on multiple spectral features. Then, a cloud-free reference image of target areas is synthesized by weighted averaging time-series images in which cloud pixels are ignored. Thirdly, the refined cloud detection results are acquired by multi-temporal analysis based on the reference image. Finally, detected clouds are removed via multi-temporal linear regression. The results of a case application in Hubei province indicate that the proposed multi-temporal cloud detection and removal method is effective and promising for large-scale land resource investigation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20detection" title="cloud detection">cloud detection</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20remove" title=" cloud remove"> cloud remove</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-temporal%20imagery" title=" multi-temporal imagery"> multi-temporal imagery</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20resources%20investigation" title=" land resources investigation"> land resources investigation</a> </p> <a href="https://publications.waset.org/abstracts/90359/multi-temporal-cloud-detection-and-removal-in-satellite-imagery-for-land-resources-investigation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90359.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">278</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2882</span> Level Set Based Extraction and Update of Lake Contours Using Multi-Temporal Satellite Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yindi%20Zhao">Yindi Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Yun%20Zhang"> Yun Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Silu%20Xia"> Silu Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=Lixin%20Wu"> Lixin Wu </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The contours and areas of water surfaces, especially lakes, often change due to natural disasters and construction activities. It is an effective way to extract and update water contours from satellite images using image processing algorithms. However, to produce optimal water surface contours that are close to true boundaries is still a challenging task. This paper compares the performances of three different level set models, including the Chan-Vese (CV) model, the signed pressure force (SPF) model, and the region-scalable fitting (RSF) energy model for extracting lake contours. After experiment testing, it is indicated that the RSF model, in which a region-scalable fitting (RSF) energy functional is defined and incorporated into a variational level set formulation, is superior to CV and SPF, and it can get desirable contour lines when there are “holes” in the regions of waters, such as the islands in the lake. Therefore, the RSF model is applied to extracting lake contours from Landsat satellite images. Four temporal Landsat satellite images of the years of 2000, 2005, 2010, and 2014 are used in our study. All of them were acquired in May, with the same path/row (121/036) covering Xuzhou City, Jiangsu Province, China. Firstly, the near infrared (NIR) band is selected for water extraction. Image registration is conducted on NIR bands of different temporal images for information update, and linear stretching is also done in order to distinguish water from other land cover types. Then for the first temporal image acquired in 2000, lake contours are extracted via the RSF model with initialization of user-defined rectangles. Afterwards, using the lake contours extracted the previous temporal image as the initialized values, lake contours are updated for the current temporal image by means of the RSF model. Meanwhile, the changed and unchanged lakes are also detected. The results show that great changes have taken place in two lakes, i.e. Dalong Lake and Panan Lake, and RSF can actually extract and effectively update lake contours using multi-temporal satellite image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=level%20set%20model" title="level set model">level set model</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-temporal%20image" title=" multi-temporal image"> multi-temporal image</a>, <a href="https://publications.waset.org/abstracts/search?q=lake%20contour%20extraction" title=" lake contour extraction"> lake contour extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=contour%20update" title=" contour update"> contour update</a> </p> <a href="https://publications.waset.org/abstracts/25775/level-set-based-extraction-and-update-of-lake-contours-using-multi-temporal-satellite-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25775.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">366</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2881</span> Research on the Strategy of Orbital Avoidance for Optical Remote Sensing Satellite</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zheng%20DianXun">Zheng DianXun</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng%20Bo"> Cheng Bo</a>, <a href="https://publications.waset.org/abstracts/search?q=Lin%20Hetong"> Lin Hetong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper focuses on the orbit avoidance strategies of optical remote sensing satellite. The optical remote sensing satellite, moving along the Sun-synchronous orbit, is equipped with laser warning equipment to alert CCD camera from laser attacks. There are three ways to protect the CCD camera: closing the camera cover, satellite attitude maneuver and satellite orbit avoidance. In order to enhance the safety of optical remote sensing satellite in orbit, this paper explores the strategy of satellite avoidance. The avoidance strategy is expressed as the evasion of pre-determined target points in the orbital coordinates of virtual satellite. The so-called virtual satellite is a passive vehicle which superposes the satellite at the initial stage of avoidance. The target points share the consistent cycle time and the same semi-major axis with the virtual satellite, which ensures the properties of the satellite’s Sun-synchronous orbit remain unchanged. Moreover, to further strengthen the avoidance capability of satellite, it can perform multi-target-points avoid maneuvers. On occasions of fulfilling the satellite orbit tasks, the orbit can be restored back to virtual satellite through orbit maneuvers. Thereinto, the avoid maneuvers adopts pulse guidance. And the fuel consumption is also optimized. The avoidance strategy discussed in this article is applicable to optical remote sensing satellite when it is encountered with hostile attack of space-based laser anti-satellite. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optical%20remote%20sensing%20satellite" title="optical remote sensing satellite">optical remote sensing satellite</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20avoidance" title=" satellite avoidance"> satellite avoidance</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20satellite" title=" virtual satellite"> virtual satellite</a>, <a href="https://publications.waset.org/abstracts/search?q=avoid%20target-point" title=" avoid target-point"> avoid target-point</a>, <a href="https://publications.waset.org/abstracts/search?q=avoid%20maneuver" title=" avoid maneuver"> avoid maneuver</a> </p> <a href="https://publications.waset.org/abstracts/34217/research-on-the-strategy-of-orbital-avoidance-for-optical-remote-sensing-satellite" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34217.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">404</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2880</span> The Strategy of Orbit Avoidance for Optical Remote Sensing Satellite</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dianxun%20Zheng">Dianxun Zheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Wuxing%20Jing"> Wuxing Jing</a>, <a href="https://publications.waset.org/abstracts/search?q=Lin%20Hetong"> Lin Hetong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optical remote sensing satellite, always running on the Sun-synchronous orbit, equipped laser warning equipment to alert CCD camera from laser attack. There have three ways to protect the CCD camera, closing the camera cover satellite attitude maneuver and satellite orbit avoidance. In order to enhance the safety of optical remote sensing satellite in orbit, this paper explores the strategy of satellite avoidance. The avoidance strategy is expressed as the evasion of pre-determined target points in the orbital coordinates of virtual satellite. The so-called virtual satellite is a passive vehicle which superposes a satellite at the initial stage of avoidance. The target points share the consistent cycle time and the same semi-major axis with the virtual satellite, which ensures the properties of the Sun-synchronous orbit remain unchanged. Moreover, to further strengthen the avoidance capability of satellite, it can perform multi-object avoid maneuvers. On occasions of fulfilling the orbit tasks of the satellite, the orbit can be restored back to virtual satellite through orbit maneuvers. There into, the avoid maneuvers adopts pulse guidance. and the fuel consumption is also optimized. The avoidance strategy discussed in this article is applicable to avoidance for optical remote sensing satellite when encounter the laser hostile attacks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optical%20remote%20sensing%20satellite" title="optical remote sensing satellite">optical remote sensing satellite</a>, <a href="https://publications.waset.org/abstracts/search?q=always%20running%20on%20the%20sun-synchronous" title=" always running on the sun-synchronous"> always running on the sun-synchronous</a> </p> <a href="https://publications.waset.org/abstracts/31188/the-strategy-of-orbit-avoidance-for-optical-remote-sensing-satellite" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31188.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">400</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2879</span> Routing in IP/LEO Satellite Communication Systems: Past, Present and Future</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Hussein">Mohammed Hussein</a>, <a href="https://publications.waset.org/abstracts/search?q=Abualseoud%20Hanani"> Abualseoud Hanani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Low Earth Orbit (LEO) satellite constellation system, routing data from the source all the way to the destination constitutes a daunting challenge because LEO satellite constellation resources are spare and the high speed movement of LEO satellites results in a highly dynamic network topology. This situation limits the applicability of traditional routing approaches that rely on exchanging topology information upon change or setup of a connection. Consequently, in recent years, many routing algorithms and implementation strategies for satellite constellation networks with Inter Satellite Links (ISLs) have been proposed. In this article, we summarize and classify some of the most representative solutions according to their objectives, and discuss their advantages and disadvantages. Finally, with a look into the future, we present some of the new challenges and opportunities for LEO satellite constellations in general and routing protocols in particular. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=LEO%20satellite%20constellations" title="LEO satellite constellations">LEO satellite constellations</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20topology" title=" dynamic topology"> dynamic topology</a>, <a href="https://publications.waset.org/abstracts/search?q=IP%20routing" title=" IP routing"> IP routing</a>, <a href="https://publications.waset.org/abstracts/search?q=inter-satellite-links" title=" inter-satellite-links"> inter-satellite-links</a> </p> <a href="https://publications.waset.org/abstracts/54344/routing-in-ipleo-satellite-communication-systems-past-present-and-future" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54344.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">381</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2878</span> Stability Assessment of Chamshir Dam Based on DEM, South West Zagros</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rezvan%20Khavari">Rezvan Khavari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Zagros fold-thrust belt in SW Iran is a part of the Alpine-Himalayan system which consists of a variety of structures with different sizes or geometries. The study area is Chamshir Dam, which is located on the Zohreh River, 20 km southeast of Gachsaran City (southwest Iran). The satellite images are valuable means available to geologists for locating geological or geomorphological features expressing regional fault or fracture systems, therefore, the satellite images were used for structural analysis of the Chamshir dam area. As well, using the DEM and geological maps, 3D Models of the area have been constructed. Then, based on these models, all the acquired fracture traces data were integrated in Geographic Information System (GIS) environment by using Arc GIS software. Based on field investigation and DEM model, main structures in the area consist of Cham Shir syncline and two fault sets, the main thrust faults with NW-SE direction and small normal faults in NE-SW direction. There are three joint sets in the study area, both of them (J1 and J3) are the main large fractures around the Chamshir dam. These fractures indeed consist with the normal faults in NE-SW direction. The third joint set in NW-SE is normal to the others. In general, according to topography, geomorphology and structural geology evidences, Chamshir dam has a potential for sliding in some parts of Gachsaran formation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DEM" title="DEM">DEM</a>, <a href="https://publications.waset.org/abstracts/search?q=chamshir%20dam" title=" chamshir dam"> chamshir dam</a>, <a href="https://publications.waset.org/abstracts/search?q=zohreh%20river" title=" zohreh river"> zohreh river</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20images" title=" satellite images "> satellite images </a> </p> <a href="https://publications.waset.org/abstracts/21593/stability-assessment-of-chamshir-dam-based-on-dem-south-west-zagros" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21593.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">482</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2877</span> Detecting the Edge of Multiple Images in Parallel</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prakash%20K.%20Aithal">Prakash K. Aithal</a>, <a href="https://publications.waset.org/abstracts/search?q=U.%20Dinesh%20Acharya"> U. Dinesh Acharya</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajesh%20Gopakumar"> Rajesh Gopakumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Edge is variation of brightness in an image. Edge detection is useful in many application areas such as finding forests, rivers from a satellite image, detecting broken bone in a medical image etc. The paper discusses about finding edge of multiple aerial images in parallel .The proposed work tested on 38 images 37 colored and one monochrome image. The time taken to process N images in parallel is equivalent to time taken to process 1 image in sequential. The proposed method achieves pixel level parallelism as well as image level parallelism. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=edge%20detection" title="edge detection">edge detection</a>, <a href="https://publications.waset.org/abstracts/search?q=multicore" title=" multicore"> multicore</a>, <a href="https://publications.waset.org/abstracts/search?q=gpu" title=" gpu"> gpu</a>, <a href="https://publications.waset.org/abstracts/search?q=opencl" title=" opencl"> opencl</a>, <a href="https://publications.waset.org/abstracts/search?q=mpi" title=" mpi"> mpi</a> </p> <a href="https://publications.waset.org/abstracts/30818/detecting-the-edge-of-multiple-images-in-parallel" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30818.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">477</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2876</span> A Method to Estimate Wheat Yield Using Landsat Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zama%20Mahmood">Zama Mahmood</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The increasing demand of food management, monitoring of the crop growth and forecasting its yield well before harvest is very important. These days, yield assessment together with monitoring of crop development and its growth are being identified with the help of satellite and remote sensing images. Studies using remote sensing data along with field survey validation reported high correlation between vegetation indices and yield. With the development of remote sensing technique, the detection of crop and its mechanism using remote sensing data on regional or global scales have become popular topics in remote sensing applications. Punjab, specially the southern Punjab region is extremely favourable for wheat production. But measuring the exact amount of wheat production is a tedious job for the farmers and workers using traditional ground based measurements. However, remote sensing can provide the most real time information. In this study, using the Normalized Differentiate Vegetation Index (NDVI) indicator developed from Landsat satellite images, the yield of wheat has been estimated during the season of 2013-2014 for the agricultural area around Bahawalpur. The average yield of the wheat was found 35 kg/acre by analysing field survey data. The field survey data is in fair agreement with the NDVI values extracted from Landsat images. A correlation between wheat production (ton) and number of wheat pixels has also been calculated which is in proportional pattern with each other. Also a strong correlation between the NDVI and wheat area was found (R2=0.71) which represents the effectiveness of the remote sensing tools for crop monitoring and production estimation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=landsat" title="landsat">landsat</a>, <a href="https://publications.waset.org/abstracts/search?q=NDVI" title=" NDVI"> NDVI</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20images" title=" satellite images"> satellite images</a>, <a href="https://publications.waset.org/abstracts/search?q=yield" title=" yield"> yield</a> </p> <a href="https://publications.waset.org/abstracts/31728/a-method-to-estimate-wheat-yield-using-landsat-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31728.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">334</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=satellite%20images&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=satellite%20images&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=satellite%20images&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=satellite%20images&page=5">5</a></li> <li class="page-item"><a class="page-link" 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