Application of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentation

In synthetic aperture radar (SAR) image segmentation field, regional algorithms have shown great potential for image segmentation. The SAR images have a multiplicity of complex texture, which are difficult to be divided as a whole. Existing algorithm may cause mixed super-pixels with different label...

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Autores principales:	Chen Zhuoran, Cong Biao, Hua Zhenxing, Cengiz Korhan, Shabaz Mohammad
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	clustering algorithm farmland sar image segmentation regional algorithms noise interference Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/2cb5a41f34e74056be60de8d74b7581a
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spelling	oai:doaj.org-article:2cb5a41f34e74056be60de8d74b7581a2021-12-05T14:10:51ZApplication of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentation2191-026X10.1515/jisys-2021-0096https://doaj.org/article/2cb5a41f34e74056be60de8d74b7581a2021-10-01T00:00:00Zhttps://doi.org/10.1515/jisys-2021-0096https://doaj.org/toc/2191-026XIn synthetic aperture radar (SAR) image segmentation field, regional algorithms have shown great potential for image segmentation. The SAR images have a multiplicity of complex texture, which are difficult to be divided as a whole. Existing algorithm may cause mixed super-pixels with different labels due to speckle noise. This study presents the technique based on organization evolution (OEA) algorithm to improve ISODATA in pixels. This approach effectively filters out the useless local information and successfully introduces the effective information. To verify the accuracy of OEA-ISO data algorithm, the segmentation effect of this algorithm is tested on SAR image and compared with other techniques. The results demonstrate that the OEA-ISO data algorithm is 10.16% more accurate than the WIPFCM algorithm, 23% more accurate than the K-means algorithm, and 27.14% more accurate than the fuzzy C-means algorithm in the light-colored farmland category. It can be seen that the OEA-ISO data algorithm introduces the pixel block strategy, which successfully reduces the noise interference in the image, and the effect is more obvious when the image background is complex.Chen ZhuoranCong BiaoHua ZhenxingCengiz KorhanShabaz MohammadDe Gruyterarticleclustering algorithmfarmlandsar image segmentationregional algorithmsnoise interferenceScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 1014-1025 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	clustering algorithm farmland sar image segmentation regional algorithms noise interference Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	clustering algorithm farmland sar image segmentation regional algorithms noise interference Science Q Electronic computers. Computer science QA75.5-76.95 Chen Zhuoran Cong Biao Hua Zhenxing Cengiz Korhan Shabaz Mohammad Application of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentation
description	In synthetic aperture radar (SAR) image segmentation field, regional algorithms have shown great potential for image segmentation. The SAR images have a multiplicity of complex texture, which are difficult to be divided as a whole. Existing algorithm may cause mixed super-pixels with different labels due to speckle noise. This study presents the technique based on organization evolution (OEA) algorithm to improve ISODATA in pixels. This approach effectively filters out the useless local information and successfully introduces the effective information. To verify the accuracy of OEA-ISO data algorithm, the segmentation effect of this algorithm is tested on SAR image and compared with other techniques. The results demonstrate that the OEA-ISO data algorithm is 10.16% more accurate than the WIPFCM algorithm, 23% more accurate than the K-means algorithm, and 27.14% more accurate than the fuzzy C-means algorithm in the light-colored farmland category. It can be seen that the OEA-ISO data algorithm introduces the pixel block strategy, which successfully reduces the noise interference in the image, and the effect is more obvious when the image background is complex.
format	article
author	Chen Zhuoran Cong Biao Hua Zhenxing Cengiz Korhan Shabaz Mohammad
author_facet	Chen Zhuoran Cong Biao Hua Zhenxing Cengiz Korhan Shabaz Mohammad
author_sort	Chen Zhuoran
title	Application of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentation
title_short	Application of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentation
title_full	Application of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentation
title_fullStr	Application of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentation
title_full_unstemmed	Application of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentation
title_sort	application of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentation
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/2cb5a41f34e74056be60de8d74b7581a
work_keys_str_mv	AT chenzhuoran applicationofclusteringalgorithmincomplexlandscapefarmlandsyntheticapertureradarimagesegmentation AT congbiao applicationofclusteringalgorithmincomplexlandscapefarmlandsyntheticapertureradarimagesegmentation AT huazhenxing applicationofclusteringalgorithmincomplexlandscapefarmlandsyntheticapertureradarimagesegmentation AT cengizkorhan applicationofclusteringalgorithmincomplexlandscapefarmlandsyntheticapertureradarimagesegmentation AT shabazmohammad applicationofclusteringalgorithmincomplexlandscapefarmlandsyntheticapertureradarimagesegmentation
_version_	1718371662964981760

Application of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentation

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