Utilizing the Principal Component Analysis on Multispectral Aerial Imagery for Identification of Underlying Structures

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Bosques-Perez"> <meta name="citation_author" content="W. Izquierdo"> <meta name="citation_author" content="H. Martin"> <meta name="citation_author" content="L. Deng"> <meta name="citation_author" content="J. Rodriguez"> <meta name="citation_author" content="T. Yan"> <meta name="citation_author" content="M. Cabrerizo"> <meta name="citation_author" content="A. Barreto"> <meta name="citation_author" content="N. Rishe"> <meta name="citation_author" content="M. 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Bosques-Perez</a>, <a href="https://publications.waset.org/search?q=W.%20Izquierdo"> W. Izquierdo</a>, <a href="https://publications.waset.org/search?q=H.%20Martin"> H. Martin</a>, <a href="https://publications.waset.org/search?q=L.%20Deng"> L. Deng</a>, <a href="https://publications.waset.org/search?q=J.%20Rodriguez"> J. Rodriguez</a>, <a href="https://publications.waset.org/search?q=T.%20Yan"> T. Yan</a>, <a href="https://publications.waset.org/search?q=M.%20Cabrerizo"> M. Cabrerizo</a>, <a href="https://publications.waset.org/search?q=A.%20Barreto"> A. Barreto</a>, <a href="https://publications.waset.org/search?q=N.%20Rishe"> N. Rishe</a>, <a href="https://publications.waset.org/search?q=M.%20Adjouadi"> M. Adjouadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Aerial imagery is a powerful tool when it comes to analyzing temporal changes in ecosystems and extracting valuable information from the observed scene. It allows us to identify and assess various elements such as objects, structures, textures, waterways, and shadows. To extract meaningful information, multispectral cameras capture data across different wavelength bands of the electromagnetic spectrum. In this study, the collected multispectral aerial images were subjected to principal component analysis (PCA) to identify independent and uncorrelated components or features that extend beyond the visible spectrum captured in standard RGB images. The results demonstrate that these principal components contain unique characteristics specific to certain wavebands, enabling effective object identification and image segmentation.</p> <iframe src="https://publications.waset.org/10013776.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Big%20data" title="Big data">Big data</a>, <a href="https://publications.waset.org/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/search?q=multispectral" title=" multispectral"> multispectral</a>, <a href="https://publications.waset.org/search?q=principal%0D%0Acomponent%20analysis." title=" principal component analysis."> principal component analysis.</a> </p> <a href="https://publications.waset.org/10013776/utilizing-the-principal-component-analysis-on-multispectral-aerial-imagery-for-identification-of-underlying-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013776/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013776/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013776/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013776/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013776/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013776/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013776/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013776/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013776/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013776/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013776.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">94</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] S. Kovbasiuk, L. Kanevskyy and M. Romanchuk, ”Object Detection Method Based on Aerial Image Instance Segmentation by Unmanned Aerial System in the Framework of Decision Making System,” 2019 3rd International Conference on Advanced Information and Communications Technologies (AICT), Lviv, Ukraine, 2019, pp. 332-335, doi: 10.1109/AIACT.2019.8847875. <br>[2] Z. Song, H. Zhang, F. Liu, S. Chen and F. Zhang, ”Unmanned Aerial Vehicle Coverage Path Planning Algorithm Based on Cellular Automata,” 2018 International Conference on Information Systems and Computer Aided Education (ICISCAE), Changchun, China, 2018, pp. 371-374, doi: 10.1109/ICISCAE.2018.8666895. <br>[3] I. Yoon, M. H. Hayes and J. Paik, ”Wavelength-adaptive image formation model and geometric classification for defogging unmanned aerial vehicle images,” 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, 2013, pp. 2454-2458, doi: 10.1109/ICASSP.2013.6638096. <br>[4] P. Duan, S. 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Utilizing the Principal Component Analysis on Multispectral Aerial Imagery for Identification of Underlying Structures