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detection</title> <meta name="description" content="Search results for: change detection"> <meta name="keywords" content="change detection"> <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="change detection"> <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> 10173</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: change detection</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10173</span> Change Detection Method Based on Scale-Invariant Feature Transformation Keypoints and Segmentation for Synthetic Aperture Radar Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lan%20Du">Lan Du</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Wang"> Yan Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hui%20Dai"> Hui Dai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Synthetic aperture radar (SAR) image change detection has recently become a challenging problem owing to the existence of speckle noises. In this paper, an unsupervised distribution-free change detection for SAR image based on scale-invariant feature transform (SIFT) keypoints and segmentation is proposed. Firstly, the noise-robust SIFT keypoints which reveal the blob-like structures in an image are extracted in the log-ratio image to reduce the detection range. Then, different from the traditional change detection which directly obtains the change-detection map from the difference image, segmentation is made around the extracted keypoints in the two original multitemporal SAR images to obtain accurate changed region. At last, the change-detection map is generated by comparing the two segmentations. Experimental results on the real SAR image dataset demonstrate the effectiveness of the proposed method. <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=Synthetic%20Aperture%20Radar%20%28SAR%29" title=" Synthetic Aperture Radar (SAR)"> Synthetic Aperture Radar (SAR)</a>, <a href="https://publications.waset.org/abstracts/search?q=Scale-Invariant%20Feature%20Transformation%20%28SIFT%29" title=" Scale-Invariant Feature Transformation (SIFT)"> Scale-Invariant Feature Transformation (SIFT)</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/66992/change-detection-method-based-on-scale-invariant-feature-transformation-keypoints-and-segmentation-for-synthetic-aperture-radar-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66992.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">386</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">10172</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">10171</span> Land Use Change Detection Using Satellite Images for Najran City, Kingdom of Saudi Arabia (KSA)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ismail%20Elkhrachy">Ismail Elkhrachy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Determination of land use changing is an important component of regional planning for applications ranging from urban fringe change detection to monitoring change detection of land use. This data are very useful for natural resources management.On the other hand, the technologies and methods of change detection also have evolved dramatically during past 20 years. So it has been well recognized that the change detection had become the best methods for researching dynamic change of land use by multi-temporal remotely-sensed data. The objective of this paper is to assess, evaluate and monitor land use change surrounding the area of Najran city, Kingdom of Saudi Arabia (KSA) using Landsat images (June 23, 2009) and ETM+ image(June. 21, 2014). The post-classification change detection technique was applied. At last,two-time subset images of Najran city are compared on a pixel-by-pixel basis using the post-classification comparison method and the from-to change matrix is produced, the land use change information obtained.Three classes were obtained, urban, bare land and agricultural land from unsupervised classification method by using Erdas Imagine and ArcGIS software. Accuracy assessment of classification has been performed before calculating change detection for study area. The obtained accuracy is between 61% to 87% percent for all the classes. Change detection analysis shows that rapid growth in urban area has been increased by 73.2%, the agricultural area has been decreased by 10.5 % and barren area reduced by 7% between 2009 and 2014. The quantitative study indicated that the area of urban class has unchanged by 58.2 km〗^2, gained 70.3 〖km〗^2 and lost 16 〖km〗^2. For bare land class 586.4〖km〗^2 has unchanged, 53.2〖km〗^2 has gained and 101.5〖km〗^2 has lost. While agriculture area class, 20.2〖km〗^2 has unchanged, 31.2〖km〗^2 has gained and 37.2〖km〗^2 has lost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=land%20use" title="land use">land use</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=change%20detection" title=" change detection"> change detection</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=image%20classification" title=" image classification"> image classification</a> </p> <a href="https://publications.waset.org/abstracts/22554/land-use-change-detection-using-satellite-images-for-najran-city-kingdom-of-saudi-arabia-ksa" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22554.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">522</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">10170</span> Modified CUSUM Algorithm for Gradual Change Detection in a Time Series Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Victoria%20Siriaki%20Jorry">Victoria Siriaki Jorry</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20S.%20Mbalawata"> I. S. Mbalawata</a>, <a href="https://publications.waset.org/abstracts/search?q=Hayong%20Shin"> Hayong Shin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main objective in a change detection problem is to develop algorithms for efficient detection of gradual and/or abrupt changes in the parameter distribution of a process or time series data. In this paper, we present a modified cumulative (MCUSUM) algorithm to detect the start and end of a time-varying linear drift in mean value of a time series data based on likelihood ratio test procedure. The design, implementation and performance of the proposed algorithm for a linear drift detection is evaluated and compared to the existing CUSUM algorithm using different performance measures. An approach to accurately approximate the threshold of the MCUSUM is also provided. Performance of the MCUSUM for gradual change-point detection is compared to that of standard cumulative sum (CUSUM) control chart designed for abrupt shift detection using Monte Carlo Simulations. In terms of the expected time for detection, the MCUSUM procedure is found to have a better performance than a standard CUSUM chart for detection of the gradual change in mean. The algorithm is then applied and tested to a randomly generated time series data with a gradual linear trend in mean to demonstrate its usefulness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=CUSUM%20control%20chart" title=" CUSUM control chart"> CUSUM control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=gradual%20change%20detection" title=" gradual change detection"> gradual change detection</a>, <a href="https://publications.waset.org/abstracts/search?q=likelihood%20ratio%20test" title=" likelihood ratio test"> likelihood ratio test</a> </p> <a href="https://publications.waset.org/abstracts/70339/modified-cusum-algorithm-for-gradual-change-detection-in-a-time-series-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70339.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">298</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">10169</span> Continuous Land Cover Change Detection in Subtropical Thicket Ecosystems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Craig%20Mahlasi">Craig Mahlasi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Subtropical Thicket Biome has been in peril of transformation. Estimates indicate that as much as 63% of the Subtropical Thicket Biome is severely degraded. Agricultural expansion is the main driver of transformation. While several studies have sought to document and map the long term transformations, there is a lack of information on disturbance events that allow for timely intervention by authorities. Furthermore, tools that seek to perform continuous land cover change detection are often developed for forests and thus tend to perform poorly in thicket ecosystems. This study investigates the utility of Earth Observation data for continuous land cover change detection in Subtropical Thicket ecosystems. Temporal Neural Networks are implemented on a time series of Sentinel-2 observations. The model obtained 0.93 accuracy, a recall score of 0.93, and a precision score of 0.91 in detecting Thicket disturbances. The study demonstrates the potential of continuous land cover change in Subtropical Thicket ecosystems. <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=land%20cover%20change%20detection" title=" land cover change detection"> land cover change detection</a>, <a href="https://publications.waset.org/abstracts/search?q=subtropical%20thickets" title=" subtropical thickets"> subtropical thickets</a>, <a href="https://publications.waset.org/abstracts/search?q=near-real%20time" title=" near-real time"> near-real time</a> </p> <a href="https://publications.waset.org/abstracts/144799/continuous-land-cover-change-detection-in-subtropical-thicket-ecosystems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144799.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">162</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">10168</span> Change Point Analysis in Average Ozone Layer Temperature Using Exponential Lomax Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amjad%20Abdullah">Amjad Abdullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Amjad%20Yahya"> Amjad Yahya</a>, <a href="https://publications.waset.org/abstracts/search?q=Bushra%20Aljohani"> Bushra Aljohani</a>, <a href="https://publications.waset.org/abstracts/search?q=Amani%20Alghamdi"> Amani Alghamdi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Change point detection is an important part of data analysis. The presence of a change point refers to a significant change in the behavior of a time series. In this article, we examine the detection of multiple change points of parameters of the exponential Lomax distribution, which is broad and flexible compared with other distributions while fitting data. We used the Schwarz information criterion and binary segmentation to detect multiple change points in publicly available data on the average temperature in the ozone layer. The change points were successfully located. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binary%20segmentation" title="binary segmentation">binary segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=change%20point" title=" change point"> change point</a>, <a href="https://publications.waset.org/abstracts/search?q=exponentialLomax%20%20distribution" title=" exponentialLomax distribution"> exponentialLomax distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20%20criterion" title=" information criterion"> information criterion</a> </p> <a href="https://publications.waset.org/abstracts/145911/change-point-analysis-in-average-ozone-layer-temperature-using-exponential-lomax-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145911.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">174</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">10167</span> Multitemporal Satellite Images for Agriculture Change Detection in Al Jouf Region, Saudi Arabia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20A.%20Aldosari">Ali A. Aldosari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Change detection of Earth surface features is extremely important for better understanding of our environment in order to promote better decision making. Al-Jawf is remarkable for its abundant agricultural water where there is fertile agricultural land due largely to underground water. As result, this region has large areas of cultivation of dates, olives and fruits trees as well as other agricultural products such as Alfa Alfa and wheat. However this agricultural area was declined due to the reduction of government supports in the last decade. This reduction was not officially recorded or measured in this region at large scale or governorate level. Remote sensing data are primary sources extensively used for change detection in agriculture applications. This study is applied the technology of GIS and used the Normalized Difference Vegetation Index (NDVI) which can be used to measure and analyze the spatial and temporal changes in the agriculture areas in the Aljouf region. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatial%20analysis" title="spatial analysis">spatial analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=geographical%20information%20system" title=" geographical information system"> geographical information system</a>, <a href="https://publications.waset.org/abstracts/search?q=change%20detection" title=" change detection"> change detection</a> </p> <a href="https://publications.waset.org/abstracts/23940/multitemporal-satellite-images-for-agriculture-change-detection-in-al-jouf-region-saudi-arabia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23940.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">402</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">10166</span> Determining Abnomal Behaviors in UAV Robots for Trajectory Control in Teleoperation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kiwon%20Yeom">Kiwon Yeom</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Change points are abrupt variations in a data sequence. Detection of change points is useful in modeling, analyzing, and predicting time series in application areas such as robotics and teleoperation. In this paper, a change point is defined to be a discontinuity in one of its derivatives. This paper presents a reliable method for detecting discontinuities within a three-dimensional trajectory data. The problem of determining one or more discontinuities is considered in regular and irregular trajectory data from teleoperation. We examine the geometric detection algorithm and illustrate the use of the method on real data examples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=change%20point" title="change point">change point</a>, <a href="https://publications.waset.org/abstracts/search?q=discontinuity" title=" discontinuity"> discontinuity</a>, <a href="https://publications.waset.org/abstracts/search?q=teleoperation" title=" teleoperation"> teleoperation</a>, <a href="https://publications.waset.org/abstracts/search?q=abrupt%20variation" title=" abrupt variation"> abrupt variation</a> </p> <a href="https://publications.waset.org/abstracts/78413/determining-abnomal-behaviors-in-uav-robots-for-trajectory-control-in-teleoperation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78413.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">167</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">10165</span> Toward Subtle Change Detection and Quantification in Magnetic Resonance Neuroimaging</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Esmaeilpour">Mohammad Esmaeilpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the important open problems in the field of medical image processing is detection and quantification of small changes. In this poster, we try to investigate that, how the algebraic decomposition techniques can be used for semiautomatically detecting and quantifying subtle changes in Magnetic Resonance (MR) neuroimaging volumes. We mostly focus on the low-rank values of the matrices achieved from decomposing MR image pairs during a period of time. Besides, a skillful neuroradiologist will help the algorithm to distinguish between noises and small changes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20neuroimaging" title="magnetic resonance neuroimaging">magnetic resonance neuroimaging</a>, <a href="https://publications.waset.org/abstracts/search?q=subtle%20change%20detection%20and%20quantification" title=" subtle change detection and quantification"> subtle change detection and quantification</a>, <a href="https://publications.waset.org/abstracts/search?q=algebraic%20decomposition" title=" algebraic decomposition"> algebraic decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=basis%20functions" title=" basis functions"> basis functions</a> </p> <a href="https://publications.waset.org/abstracts/32372/toward-subtle-change-detection-and-quantification-in-magnetic-resonance-neuroimaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32372.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">474</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">10164</span> Islanding Detection of Wind Turbine by Rate of Change of Frequency (ROCOF) and Rate of change of Power (ROCOP) Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vipulkumar%20Jagodana">Vipulkumar Jagodana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently the use of renewable sources has increased, these sources include fuel cell, photo voltaic, and wind turbine. Islanding occurs when one portion of grid is isolated from remaining grid. Use of the renewable sources can provide continuous power to isolated portion in islanding condition. One of the common renewable sources is wind generation using wind turbine. The efficiency of wind generation can be increased in combination with conventional sources. When islanding occurs, few parameters change which may be frequency, voltage, active power, and harmonics. According to large change in one of these parameters islanding is detected. In this paper, two passive methods Rate of Change of Frequency (ROCOF) and Rate of change of Power (ROCOP) have been implemented for islanding detection of small wind-turbine. Islanding detection of both methods have been simulated in PSCAD. Simulation results show at different islanding inception angle response of ROCOF and ROCOP. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=islanding" title="islanding">islanding</a>, <a href="https://publications.waset.org/abstracts/search?q=adopted%20methods" title=" adopted methods"> adopted methods</a>, <a href="https://publications.waset.org/abstracts/search?q=PSCAD%20simulation" title=" PSCAD simulation"> PSCAD simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=comparison" title=" comparison"> comparison</a> </p> <a href="https://publications.waset.org/abstracts/52214/islanding-detection-of-wind-turbine-by-rate-of-change-of-frequency-rocof-and-rate-of-change-of-power-rocop-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52214.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">225</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">10163</span> Change Detection Analysis on Support Vector Machine Classifier of Land Use and Land Cover Changes: Case Study on Yangon</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khin%20Mar%20Yee">Khin Mar Yee</a>, <a href="https://publications.waset.org/abstracts/search?q=Mu%20Mu%20Than"> Mu Mu Than</a>, <a href="https://publications.waset.org/abstracts/search?q=Kyi%20Lint"> Kyi Lint</a>, <a href="https://publications.waset.org/abstracts/search?q=Aye%20Aye%20Oo"> Aye Aye Oo</a>, <a href="https://publications.waset.org/abstracts/search?q=Chan%20Mya%20Hmway"> Chan Mya Hmway</a>, <a href="https://publications.waset.org/abstracts/search?q=Khin%20Zar%20Chi%20Winn"> Khin Zar Chi Winn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The dynamic changes of Land Use and Land Cover (LULC) changes in Yangon have generally resulted the improvement of human welfare and economic development since the last twenty years. Making map of LULC is crucially important for the sustainable development of the environment. However, the exactly data on how environmental factors influence the LULC situation at the various scales because the nature of the natural environment is naturally composed of non-homogeneous surface features, so the features in the satellite data also have the mixed pixels. The main objective of this study is to the calculation of accuracy based on change detection of LULC changes by Support Vector Machines (SVMs). For this research work, the main data was satellite images of 1996, 2006 and 2015. Computing change detection statistics use change detection statistics to compile a detailed tabulation of changes between two classification images and Support Vector Machines (SVMs) process was applied with a soft approach at allocation as well as at a testing stage and to higher accuracy. The results of this paper showed that vegetation and cultivated area were decreased (average total 29 % from 1996 to 2015) because of conversion to the replacing over double of the built up area (average total 30 % from 1996 to 2015). The error matrix and confidence limits led to the validation of the result for LULC mapping. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=land%20use%20and%20land%20cover%20change" title="land use and land cover change">land use and land cover change</a>, <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=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines" title=" support vector machines"> support vector machines</a> </p> <a href="https://publications.waset.org/abstracts/105016/change-detection-analysis-on-support-vector-machine-classifier-of-land-use-and-land-cover-changes-case-study-on-yangon" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105016.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">138</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">10162</span> Application of Change Detection Techniques in Monitoring Environmental Phenomena: A Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20Garba">T. Garba</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Y.%20Babanyara"> Y. Y. Babanyara</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20O.%20Quddus"> T. O. Quddus</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20K.%20Mukatari"> A. K. Mukatari </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human activities make environmental parameters in order to keep on changing globally. While some changes are necessary and beneficial to flora and fauna, others have serious consequences threatening the survival of their natural habitat if these changes are not properly monitored and mitigated. In-situ assessments are characterized by many challenges due to the absence of time series data and sometimes areas to be observed or monitored are inaccessible. Satellites Remote Sensing provide us with the digital images of same geographic areas within a pre-defined interval. This makes it possible to monitor and detect changes of environmental phenomena. This paper, therefore, reviewed the commonly use changes detection techniques globally such as image differencing, image rationing, image regression, vegetation index difference, change vector analysis, principal components analysis, multidate classification, post-classification comparison, and visual interpretation. The paper concludes by suggesting the use of more than one technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=environmental%20phenomena" title="environmental phenomena">environmental phenomena</a>, <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=monitor" title=" monitor"> monitor</a>, <a href="https://publications.waset.org/abstracts/search?q=techniques" title=" techniques"> techniques</a> </p> <a href="https://publications.waset.org/abstracts/12461/application-of-change-detection-techniques-in-monitoring-environmental-phenomena-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12461.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">10161</span> The Role of Emotion in Attention Allocation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michaela%20Porubanova">Michaela Porubanova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this exploratory study to examine the effects of emotional significance on change detection using the flicker paradigm, three different categories of scenes were randomly presented (neutral, positive and negative) in three different blocks. We hypothesized that because of the different effects on attention, performance in change detection tasks differs for scenes with different effective values. We found the greatest accuracy of change detection was for changes occurring in positive and negative scenes (compared with neutral scenes). Secondly and most importantly, changes in negative scenes (and also positive scenes, though not with statistical significance) were detected faster than changes in neutral scenes. Interestingly, women were less accurate than men in detecting changes in emotionally significant scenes (both negative and positive), i.e., women detected fewer changes in emotional scenes in the time limit of 40s. But on the other hand, women were quicker to detect changes in positive and negative images than men. The study makes important contributions to the area of the role of emotions on information processing. The role of emotion in attention will be discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attention" title="attention">attention</a>, <a href="https://publications.waset.org/abstracts/search?q=emotion" title=" emotion"> emotion</a>, <a href="https://publications.waset.org/abstracts/search?q=flicker%20task" title=" flicker task"> flicker task</a>, <a href="https://publications.waset.org/abstracts/search?q=IAPS" title=" IAPS"> IAPS</a> </p> <a href="https://publications.waset.org/abstracts/10319/the-role-of-emotion-in-attention-allocation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10319.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">10160</span> The Guaranteed Detection of the Seismoacoustic Emission Source in the C-OTDR Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andrey%20V.%20Timofeev">Andrey V. Timofeev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A method is proposed for stable detection of seismoacoustic sources in C-OTDR systems that guarantee given upper bounds for probabilities of type I and type II errors. Properties of the proposed method are rigorously proved. The results of practical applications of the proposed method in a real C-OTDR-system are presented in this. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=guaranteed%20detection" title="guaranteed detection">guaranteed detection</a>, <a href="https://publications.waset.org/abstracts/search?q=C-OTDR%20systems" title=" C-OTDR systems"> C-OTDR systems</a>, <a href="https://publications.waset.org/abstracts/search?q=change%20point" title=" change point"> change point</a>, <a href="https://publications.waset.org/abstracts/search?q=interval%20estimation" title=" interval estimation"> interval estimation</a> </p> <a href="https://publications.waset.org/abstracts/8400/the-guaranteed-detection-of-the-seismoacoustic-emission-source-in-the-c-otdr-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8400.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">256</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">10159</span> A Novel Spectral Index for Automatic Shadow Detection in Urban Mapping Based on WorldView-2 Satellite Imagery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kaveh%20Shahi">Kaveh Shahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Helmi%20Z.%20M.%20Shafri"> Helmi Z. M. Shafri</a>, <a href="https://publications.waset.org/abstracts/search?q=Ebrahim%20Taherzadeh"> Ebrahim Taherzadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In remote sensing, shadow causes problems in many applications such as change detection and classification. It is caused by objects which are elevated, thus can directly affect the accuracy of information. For these reasons, it is very important to detect shadows particularly in urban high spatial resolution imagery which created a significant problem. This paper focuses on automatic shadow detection based on a new spectral index for multispectral imagery known as Shadow Detection Index (SDI). The new spectral index was tested on different areas of World-View 2 images and the results demonstrated that the new spectral index has a massive potential to extract shadows effectively and automatically. <p class="card-text"><strong>Keywords:</strong> <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=shadow%20detection" title=" shadow detection"> shadow detection</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=World-View%202" title=" World-View 2"> World-View 2</a> </p> <a href="https://publications.waset.org/abstracts/13500/a-novel-spectral-index-for-automatic-shadow-detection-in-urban-mapping-based-on-worldview-2-satellite-imagery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13500.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">538</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">10158</span> Infrastructure Change Monitoring Using Multitemporal Multispectral Satellite Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=U.%20Datta">U. Datta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main objective of this study is to find a suitable approach to monitor the land infrastructure growth over a period of time using multispectral satellite images. Bi-temporal change detection method is unable to indicate the continuous change occurring over a long period of time. To achieve this objective, the approach used here estimates a statistical model from series of multispectral image data over a long period of time, assuming there is no considerable change during that time period and then compare it with the multispectral image data obtained at a later time. The change is estimated pixel-wise. Statistical composite hypothesis technique is used for estimating pixel based change detection in a defined region. The generalized likelihood ratio test (GLRT) is used to detect the changed pixel from probabilistic estimated model of the corresponding pixel. The changed pixel is detected assuming that the images have been co-registered prior to estimation. To minimize error due to co-registration, 8-neighborhood pixels around the pixel under test are also considered. The multispectral images from Sentinel-2 and Landsat-8 from 2015 to 2018 are used for this purpose. There are different challenges in this method. First and foremost challenge is to get quite a large number of datasets for multivariate distribution modelling. A large number of images are always discarded due to cloud coverage. Due to imperfect modelling there will be high probability of false alarm. Overall conclusion that can be drawn from this work is that the probabilistic method described in this paper has given some promising results, which need to be pursued further. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=co-registration" title="co-registration">co-registration</a>, <a href="https://publications.waset.org/abstracts/search?q=GLRT" title=" GLRT"> GLRT</a>, <a href="https://publications.waset.org/abstracts/search?q=infrastructure%20growth" title=" infrastructure growth"> infrastructure growth</a>, <a href="https://publications.waset.org/abstracts/search?q=multispectral" title=" multispectral"> multispectral</a>, <a href="https://publications.waset.org/abstracts/search?q=multitemporal" title=" multitemporal"> multitemporal</a>, <a href="https://publications.waset.org/abstracts/search?q=pixel-based%20change%20detection" title=" pixel-based change detection"> pixel-based change detection</a> </p> <a href="https://publications.waset.org/abstracts/117430/infrastructure-change-monitoring-using-multitemporal-multispectral-satellite-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/117430.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">134</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">10157</span> Adaptive Nonparametric Approach for Guaranteed Real-Time Detection of Targeted Signals in Multichannel Monitoring Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andrey%20V.%20Timofeev">Andrey V. Timofeev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An adaptive nonparametric method is proposed for stable real-time detection of seismoacoustic sources in multichannel C-OTDR systems with a significant number of channels. This method guarantees given upper boundaries for probabilities of Type I and Type II errors. Properties of the proposed method are rigorously proved. The results of practical applications of the proposed method in a real C-OTDR-system are presented in this report. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=guaranteed%20detection" title="guaranteed detection">guaranteed detection</a>, <a href="https://publications.waset.org/abstracts/search?q=multichannel%20monitoring%20systems" title=" multichannel monitoring systems"> multichannel monitoring systems</a>, <a href="https://publications.waset.org/abstracts/search?q=change%20point" title=" change point"> change point</a>, <a href="https://publications.waset.org/abstracts/search?q=interval%20estimation" title=" interval estimation"> interval estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20detection" title=" adaptive detection"> adaptive detection</a> </p> <a href="https://publications.waset.org/abstracts/21976/adaptive-nonparametric-approach-for-guaranteed-real-time-detection-of-targeted-signals-in-multichannel-monitoring-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21976.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">447</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">10156</span> Urban Change Detection and Pattern Analysis Using Satellite Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shivani%20Jha">Shivani Jha</a>, <a href="https://publications.waset.org/abstracts/search?q=Klaus%20Baier"> Klaus Baier</a>, <a href="https://publications.waset.org/abstracts/search?q=Rafiq%20Azzam"> Rafiq Azzam</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramakar%20Jha"> Ramakar Jha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In India, generally people migrate from rural area to the urban area for better infra-structural facilities, high standard of living, good job opportunities and advanced transport/communication availability. In fact, unplanned urban development due to migration of people causes seriou damage to the land use, water pollution and available water resources. In the present work, an attempt has been made to use satellite data of different years for urban change detection of Chennai metropolitan city along with pattern analysis to generate future scenario of urban development using buffer zoning in GIS environment. In the analysis, SRTM (30m) elevation data and IRS-1C satellite data for the years 1990, 2000, and 2014, are used. The flow accumulation, aspect, flow direction and slope maps developed using SRTM 30 m data are very useful for finding suitable urban locations for industrial setup and urban settlements. Normalized difference vegetation index (NDVI) and Principal Component Analysis (PCA) have been used in ERDAS imagine software for change detection in land use of Chennai metropolitan city. It has been observed that the urban area has increased exponentially in Chennai metropolitan city with significant decrease in agriculture and barren lands. However, the water bodies located in the study regions are protected and being used as freshwater for drinking purposes. Using buffer zone analysis in GIS environment, it has been observed that the development has taken place in south west direction significantly and will do so in future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=urban%20change" title="urban change">urban change</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20data" title=" satellite data"> satellite data</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20Chennai%20metropolis" title=" the Chennai metropolis"> the Chennai metropolis</a>, <a href="https://publications.waset.org/abstracts/search?q=change%20detection" title=" change detection"> change detection</a> </p> <a href="https://publications.waset.org/abstracts/22093/urban-change-detection-and-pattern-analysis-using-satellite-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22093.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">408</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">10155</span> Refractometric Optical Sensing by Using Photonics Mach–Zehnder Interferometer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gong%20Zhang">Gong Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hong%20Cai"> Hong Cai</a>, <a href="https://publications.waset.org/abstracts/search?q=Bin%20Dong"> Bin Dong</a>, <a href="https://publications.waset.org/abstracts/search?q=Jifang%20Tao"> Jifang Tao</a>, <a href="https://publications.waset.org/abstracts/search?q=Aiqun%20Liu"> Aiqun Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dim-Lee%20Kwong"> Dim-Lee Kwong</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuandong%20Gu"> Yuandong Gu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An on-chip refractive index sensor with high sensitivity and large measurement range is demonstrated in this paper. The sensing structures are based on Mach-Zehnder interferometer configuration, built on the SOI substrate. The wavelength sensitivity of the sensor is estimated to be 3129 nm/RIU. Meanwhile, according to the interference pattern period changes, the measured period sensitivities are 2.9 nm/RIU (TE mode) and 4.21 nm/RIU (TM mode), respectively. As such, the wavelength shift and the period shift can be used for fine index change detection and larger index change detection, respectively. Therefore, the sensor design provides an approach for large index change measurement with high sensitivity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mach-Zehnder%20interferometer" title="Mach-Zehnder interferometer">Mach-Zehnder interferometer</a>, <a href="https://publications.waset.org/abstracts/search?q=nanotechnology" title=" nanotechnology"> nanotechnology</a>, <a href="https://publications.waset.org/abstracts/search?q=refractive%20index%20sensing" title=" refractive index sensing"> refractive index sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=sensors" title=" sensors"> sensors</a> </p> <a href="https://publications.waset.org/abstracts/37878/refractometric-optical-sensing-by-using-photonics-mach-zehnder-interferometer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37878.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">445</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">10154</span> Application of Remote Sensing and GIS in Assessing Land Cover Changes within Granite Quarries around Brits Area, South Africa</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Refilwe%20Moeletsi">Refilwe Moeletsi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dimension stone quarrying around Brits and Belfast areas started in the early 1930s and has been growing rapidly since then. Environmental impacts associated with these quarries have not been documented, and hence this study aims at detecting any change in the environment that might have been caused by these activities. Landsat images that were used to assess land use/land cover changes in Brits quarries from 1998 - 2015. A supervised classification using maximum likelihood classifier was applied to classify each image into different land use/land cover types. Classification accuracy was assessed using Google Earth™ as a source of reference data. Post-classification change detection method was used to determine changes. The results revealed significant increase in granite quarries and corresponding decrease in vegetation cover within the study region. <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=GIS" title=" GIS"> GIS</a>, <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=granite%20quarries" title=" granite quarries"> granite quarries</a> </p> <a href="https://publications.waset.org/abstracts/56098/application-of-remote-sensing-and-gis-in-assessing-land-cover-changes-within-granite-quarries-around-brits-area-south-africa" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56098.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">10153</span> Research on Placement Method of the Magnetic Flux Leakage Sensor Based on Online Detection of the Transformer Winding Deformation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wei%20Zheng">Wei Zheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Mao%20Ji"> Mao Ji</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhe%20Hou"> Zhe Hou</a>, <a href="https://publications.waset.org/abstracts/search?q=Meng%20Huang"> Meng Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Qi"> Bo Qi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The transformer is the key equipment of the power system. Winding deformation is one of the main transformer defects, and timely and effective detection of the transformer winding deformation can ensure the safe and stable operation of the transformer to the maximum extent. When winding deformation occurs, the size, shape and spatial position of the winding will change, which directly leads to the change of magnetic flux leakage distribution. Therefore, it is promising to study the online detection method of the transformer winding deformation based on magnetic flux leakage characteristics, in which the key step is to study the optimal placement method of magnetic flux leakage sensors inside the transformer. In this paper, a simulation model of the transformer winding deformation is established to obtain the internal magnetic flux leakage distribution of the transformer under normal operation and different winding deformation conditions, and the law of change of magnetic flux leakage distribution due to winding deformation is analyzed. The results show that different winding deformation leads to different characteristics of the magnetic flux leakage distribution. On this basis, an optimized placement of magnetic flux leakage sensors inside the transformer is proposed to provide a basis for the online detection method of transformer winding deformation based on the magnetic flux leakage characteristics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=magnetic%20flux%20leakage" title="magnetic flux leakage">magnetic flux leakage</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20placement%20method" title=" sensor placement method"> sensor placement method</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=winding%20deformation" title=" winding deformation"> winding deformation</a> </p> <a href="https://publications.waset.org/abstracts/136348/research-on-placement-method-of-the-magnetic-flux-leakage-sensor-based-on-online-detection-of-the-transformer-winding-deformation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136348.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">196</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">10152</span> Analysis of Spatial and Temporal Data Using Remote Sensing Technology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kapil%20Pandey">Kapil Pandey</a>, <a href="https://publications.waset.org/abstracts/search?q=Vishnu%20Goyal"> Vishnu Goyal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spatial and temporal data analysis is very well known in the field of satellite image processing. When spatial data are correlated with time, series analysis it gives the significant results in change detection studies. In this paper the GIS and Remote sensing techniques has been used to find the change detection using time series satellite imagery of Uttarakhand state during the years of 1990-2010. Natural vegetation, urban area, forest cover etc. were chosen as main landuse classes to study. Landuse/ landcover classes within several years were prepared using satellite images. Maximum likelihood supervised classification technique was adopted in this work and finally landuse change index has been generated and graphical models were used to present the changes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GIS" title="GIS">GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=landuse%2Flandcover" title=" landuse/landcover"> landuse/landcover</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20and%20temporal%20data" title=" spatial and temporal data"> spatial and temporal data</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a> </p> <a href="https://publications.waset.org/abstracts/40918/analysis-of-spatial-and-temporal-data-using-remote-sensing-technology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40918.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">433</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">10151</span> Detection of Change Points in Earthquakes Data: A Bayesian Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20A.%20Al-Awadhi">F. A. Al-Awadhi</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Al-Hulail"> D. Al-Hulail</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, we applied the Bayesian hierarchical model to detect single and multiple change points for daily earthquake body wave magnitude. The change point analysis is used in both backward (off-line) and forward (on-line) statistical research. In this study, it is used with the backward approach. Different types of change parameters are considered (mean, variance or both). The posterior model and the conditional distributions for single and multiple change points are derived and implemented using BUGS software. The model is applicable for any set of data. The sensitivity of the model is tested using different prior and likelihood functions. Using Mb data, we concluded that during January 2002 and December 2003, three changes occurred in the mean magnitude of Mb in Kuwait and its vicinity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multiple%20change%20points" title="multiple change points">multiple change points</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20Chain%20Monte%20Carlo" title=" Markov Chain Monte Carlo"> Markov Chain Monte Carlo</a>, <a href="https://publications.waset.org/abstracts/search?q=earthquake%20magnitude" title=" earthquake magnitude"> earthquake magnitude</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20Bayesian%20mode" title=" hierarchical Bayesian mode"> hierarchical Bayesian mode</a> </p> <a href="https://publications.waset.org/abstracts/21451/detection-of-change-points-in-earthquakes-data-a-bayesian-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21451.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">456</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">10150</span> Efficient Signal Detection Using QRD-M Based on Channel Condition in MIMO-OFDM System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jae-Jeong%20Kim">Jae-Jeong Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Ki-Ro%20Kim"> Ki-Ro Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyoung-Kyu%20Song"> Hyoung-Kyu Song</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose an efficient signal detector that switches M parameter of QRD-M detection scheme is proposed for MIMO-OFDM system. The proposed detection scheme calculates the threshold by 1-norm condition number and then switches M parameter of QRD-M detection scheme according to channel information. If channel condition is bad, the parameter M is set to high value to increase the accuracy of detection. If channel condition is good, the parameter M is set to low value to reduce complexity of detection. Therefore, the proposed detection scheme has better trade off between BER performance and complexity than the conventional detection scheme. The simulation result shows that the complexity of proposed detection scheme is lower than QRD-M detection scheme with similar BER performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MIMO-OFDM" title="MIMO-OFDM">MIMO-OFDM</a>, <a href="https://publications.waset.org/abstracts/search?q=QRD-M" title=" QRD-M"> QRD-M</a>, <a href="https://publications.waset.org/abstracts/search?q=channel%20condition" title=" channel condition"> channel condition</a>, <a href="https://publications.waset.org/abstracts/search?q=BER" title=" BER"> BER</a> </p> <a href="https://publications.waset.org/abstracts/3518/efficient-signal-detection-using-qrd-m-based-on-channel-condition-in-mimo-ofdm-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3518.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">370</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">10149</span> Evidence Theory Enabled Quickest Change Detection Using Big Time-Series Data from Internet of Things</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Jafari">Hossein Jafari</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiangfang%20Li"> Xiangfang Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Lijun%20Qian"> Lijun Qian</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Aved"> Alexander Aved</a>, <a href="https://publications.waset.org/abstracts/search?q=Timothy%20Kroecker"> Timothy Kroecker</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditionally in sensor networks and recently in the Internet of Things, numerous heterogeneous sensors are deployed in distributed manner to monitor a phenomenon that often can be model by an underlying stochastic process. The big time-series data collected by the sensors must be analyzed to detect change in the stochastic process as quickly as possible with tolerable false alarm rate. However, sensors may have different accuracy and sensitivity range, and they decay along time. As a result, the big time-series data collected by the sensors will contain uncertainties and sometimes they are conflicting. In this study, we present a framework to take advantage of Evidence Theory (a.k.a. Dempster-Shafer and Dezert-Smarandache Theories) capabilities of representing and managing uncertainty and conflict to fast change detection and effectively deal with complementary hypotheses. Specifically, Kullback-Leibler divergence is used as the similarity metric to calculate the distances between the estimated current distribution with the pre- and post-change distributions. Then mass functions are calculated and related combination rules are applied to combine the mass values among all sensors. Furthermore, we applied the method to estimate the minimum number of sensors needed to combine, so computational efficiency could be improved. Cumulative sum test is then applied on the ratio of pignistic probability to detect and declare the change for decision making purpose. Simulation results using both synthetic data and real data from experimental setup demonstrate the effectiveness of the presented schemes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CUSUM" title="CUSUM">CUSUM</a>, <a href="https://publications.waset.org/abstracts/search?q=evidence%20theory" title=" evidence theory"> evidence theory</a>, <a href="https://publications.waset.org/abstracts/search?q=kl%20divergence" title=" kl divergence"> kl divergence</a>, <a href="https://publications.waset.org/abstracts/search?q=quickest%20change%20detection" title=" quickest change detection"> quickest change detection</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20data" title=" time series data"> time series data</a> </p> <a href="https://publications.waset.org/abstracts/62299/evidence-theory-enabled-quickest-change-detection-using-big-time-series-data-from-internet-of-things" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62299.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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10148</span> Reduced Complexity of ML Detection Combined with DFE</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jae-Hyun%20Ro">Jae-Hyun Ro</a>, <a href="https://publications.waset.org/abstracts/search?q=Yong-Jun%20Kim"> Yong-Jun Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Chang-Bin%20Ha"> Chang-Bin Ha</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyoung-Kyu%20Song"> Hyoung-Kyu Song </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, many detection schemes have been developed to improve the error performance and to reduce the complexity. Maximum likelihood (ML) detection has optimal error performance but it has very high complexity. Thus, this paper proposes reduced complexity of ML detection combined with decision feedback equalizer (DFE). The error performance of the proposed detection scheme is higher than the conventional DFE. But the complexity of the proposed scheme is lower than the conventional ML detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=detection" title="detection">detection</a>, <a href="https://publications.waset.org/abstracts/search?q=DFE" title=" DFE"> DFE</a>, <a href="https://publications.waset.org/abstracts/search?q=MIMO-OFDM" title=" MIMO-OFDM"> MIMO-OFDM</a>, <a href="https://publications.waset.org/abstracts/search?q=ML" title=" ML"> ML</a> </p> <a href="https://publications.waset.org/abstracts/42215/reduced-complexity-of-ml-detection-combined-with-dfe" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42215.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">610</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">10147</span> Multi-Spectral Deep Learning Models for Forest Fire Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Smitha%20Haridasan">Smitha Haridasan</a>, <a href="https://publications.waset.org/abstracts/search?q=Zelalem%20Demissie"> Zelalem Demissie</a>, <a href="https://publications.waset.org/abstracts/search?q=Atri%20Dutta"> Atri Dutta</a>, <a href="https://publications.waset.org/abstracts/search?q=Ajita%20Rattani"> Ajita Rattani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aided by the wind, all it takes is one ember and a few minutes to create a wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision-based techniques have been proposed for the early detection of forest fire using video surveillance. Several computer vision-based methods have been proposed to predict and detect forest fires at various spectrums, namely, RGB, HSV, and YCbCr. The aim of this paper is to propose a multi-spectral deep learning model that combines information from different spectrums at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available datasets is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 4.68 % over those based on a single spectrum for fire detection. <p class="card-text"><strong>Keywords:</strong> <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=forest%20fire%20detection" title=" forest fire detection"> forest fire detection</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-spectral%20learning" title=" multi-spectral learning"> multi-spectral learning</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20hazard%20detection" title=" natural hazard detection"> natural hazard detection</a> </p> <a href="https://publications.waset.org/abstracts/146865/multi-spectral-deep-learning-models-for-forest-fire-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146865.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">241</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">10146</span> Detection of Nanotoxic Material Using DNA Based QCM</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Juneseok%20You">Juneseok You</a>, <a href="https://publications.waset.org/abstracts/search?q=Chanho%20Park"> Chanho Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Kuehwan%20Jang"> Kuehwan Jang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sungsoo%20Na"> Sungsoo Na</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sensing of nanotoxic materials is strongly important, as their engineering applications are growing recently and results in that nanotoxic material can harmfully influence human health and environment. In current study we report the quartz crystal microbalance (QCM)-based, in situ and real-time sensing of nanotoxic-material by frequency shift. We propose the in situ detection of nanotoxic material of zinc oxice by using QCM functionalized with a taget-specific DNA. Since the mass of a target material is comparable to that of an atom, the mass change caused by target binding to DNA on the quartz electrode is so small that it is practically difficult to detect the ions at low concentrations. In our study, we have demonstrated the in-situ and fast detection of zinc oxide using the quartz crystal microbalance (QCM). The detection was derived from the DNA hybridization between the DNA on the quartz electrode. The results suggest that QCM-based detection opens a new avenue for the development of a practical water-testing sensor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nanotoxic%20material" title="nanotoxic material">nanotoxic material</a>, <a href="https://publications.waset.org/abstracts/search?q=qcm" title=" qcm"> qcm</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency" title=" frequency"> frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=in%20situ%20sensing" title=" in situ sensing"> in situ sensing</a> </p> <a href="https://publications.waset.org/abstracts/41494/detection-of-nanotoxic-material-using-dna-based-qcm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41494.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">422</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">10145</span> Fast Accurate Detection of Frequency Jumps Using Kalman Filter with Non Linear Improvements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20E.%20Mohamed">Mahmoud E. Mohamed</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20F.%20Shalash"> Ahmed F. Shalash</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanan%20A.%20Kamal"> Hanan A. Kamal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In communication systems, frequency jump is a serious problem caused by the oscillators used. Kalman filters are used to detect that jump, Despite the tradeoff between the noise level and the speed of the detection. In this paper, An improvement is introduced in the Kalman filter, Through a nonlinear change in the bandwidth of the filter. Simulation results show a considerable improvement in the filter speed with a very low noise level. Additionally, The effect on the response to false alarms is also presented and false alarm rate show improvement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title="Kalman filter">Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=innovation" title=" innovation"> innovation</a>, <a href="https://publications.waset.org/abstracts/search?q=false%20detection" title=" false detection"> false detection</a>, <a href="https://publications.waset.org/abstracts/search?q=improvement" title=" improvement "> improvement </a> </p> <a href="https://publications.waset.org/abstracts/7978/fast-accurate-detection-of-frequency-jumps-using-kalman-filter-with-non-linear-improvements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7978.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">602</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">10144</span> Cigarette Smoke Detection Based on YOLOV3</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wei%20Li">Wei Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Tuo%20Yang"> Tuo Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to satisfy the real-time and accurate requirements of cigarette smoke detection in complex scenes, a cigarette smoke detection technology based on the combination of deep learning and color features was proposed. Firstly, based on the color features of cigarette smoke, the suspicious cigarette smoke area in the image is extracted. Secondly, combined with the efficiency of cigarette smoke detection and the problem of network overfitting, a network model for cigarette smoke detection was designed according to YOLOV3 algorithm to reduce the false detection rate. The experimental results show that the method is feasible and effective, and the accuracy of cigarette smoke detection is up to 99.13%, which satisfies the requirements of real-time cigarette smoke detection in complex scenes. <p class="card-text"><strong>Keywords:</strong> <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=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=cigarette%20smoke%20detection" title=" cigarette smoke detection"> cigarette smoke detection</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLOV3" title=" YOLOV3"> YOLOV3</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20feature%20extraction" title=" color feature extraction"> color feature extraction</a> </p> <a href="https://publications.waset.org/abstracts/159151/cigarette-smoke-detection-based-on-yolov3" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159151.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">87</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=change%20detection&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=change%20detection&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=change%20detection&page=4">4</a></li> <li class="page-item"><a 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