Target Detection in High-Resolution SAR Image via Iterating Outliers and Recursing Saliency Depth

In dealing with the problem of target detection in high-resolution Synthetic Aperture Radar (SAR) images, segmenting before detecting is the most commonly used approach. After the image is segmented by the superpixel method, the segmented area is usually a mixture of target and background, but the e...

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Autores principales:	Zongyong Cui, Yi Qin, Yating Zhong, Zongjie Cao, Haiyi Yang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	high resolution SAR image target detection conditional entropy model iterating outlier recursing saliency depth local saliency optimization Science Q
Acceso en línea:	https://doaj.org/article/638c016a73c54f92b02445b8b2fafff5
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id	oai:doaj.org-article:638c016a73c54f92b02445b8b2fafff5
record_format	dspace
spelling	oai:doaj.org-article:638c016a73c54f92b02445b8b2fafff52021-11-11T18:53:45ZTarget Detection in High-Resolution SAR Image via Iterating Outliers and Recursing Saliency Depth10.3390/rs132143152072-4292https://doaj.org/article/638c016a73c54f92b02445b8b2fafff52021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4315https://doaj.org/toc/2072-4292In dealing with the problem of target detection in high-resolution Synthetic Aperture Radar (SAR) images, segmenting before detecting is the most commonly used approach. After the image is segmented by the superpixel method, the segmented area is usually a mixture of target and background, but the existing regional feature model does not take this into account, and cannot accurately reflect the features of the SAR image. Therefore, we propose a target detection method based on iterative outliers and recursive saliency depth. At first, we use the conditional entropy to model the features of the superpixel region, which is more in line with the actual SAR image features. Then, through iterative anomaly detection, we achieve effective background selection and detection threshold design. After that, recursing saliency depth is used to enhance the effective outliers and suppress the background false alarm to realize the correction of superpixel saliency value. Finally, the local graph model is used to optimize the detection results. Compared with Constant False Alarm Rate (CFAR) and Weighted Information Entropy (WIE) methods, the results show that our method has better performance and is more in line with the actual situation.Zongyong CuiYi QinYating ZhongZongjie CaoHaiyi YangMDPI AGarticlehigh resolution SAR imagetarget detectionconditional entropy modeliterating outlierrecursing saliency depthlocal saliency optimizationScienceQENRemote Sensing, Vol 13, Iss 4315, p 4315 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	high resolution SAR image target detection conditional entropy model iterating outlier recursing saliency depth local saliency optimization Science Q
spellingShingle	high resolution SAR image target detection conditional entropy model iterating outlier recursing saliency depth local saliency optimization Science Q Zongyong Cui Yi Qin Yating Zhong Zongjie Cao Haiyi Yang Target Detection in High-Resolution SAR Image via Iterating Outliers and Recursing Saliency Depth
description	In dealing with the problem of target detection in high-resolution Synthetic Aperture Radar (SAR) images, segmenting before detecting is the most commonly used approach. After the image is segmented by the superpixel method, the segmented area is usually a mixture of target and background, but the existing regional feature model does not take this into account, and cannot accurately reflect the features of the SAR image. Therefore, we propose a target detection method based on iterative outliers and recursive saliency depth. At first, we use the conditional entropy to model the features of the superpixel region, which is more in line with the actual SAR image features. Then, through iterative anomaly detection, we achieve effective background selection and detection threshold design. After that, recursing saliency depth is used to enhance the effective outliers and suppress the background false alarm to realize the correction of superpixel saliency value. Finally, the local graph model is used to optimize the detection results. Compared with Constant False Alarm Rate (CFAR) and Weighted Information Entropy (WIE) methods, the results show that our method has better performance and is more in line with the actual situation.
format	article
author	Zongyong Cui Yi Qin Yating Zhong Zongjie Cao Haiyi Yang
author_facet	Zongyong Cui Yi Qin Yating Zhong Zongjie Cao Haiyi Yang
author_sort	Zongyong Cui
title	Target Detection in High-Resolution SAR Image via Iterating Outliers and Recursing Saliency Depth
title_short	Target Detection in High-Resolution SAR Image via Iterating Outliers and Recursing Saliency Depth
title_full	Target Detection in High-Resolution SAR Image via Iterating Outliers and Recursing Saliency Depth
title_fullStr	Target Detection in High-Resolution SAR Image via Iterating Outliers and Recursing Saliency Depth
title_full_unstemmed	Target Detection in High-Resolution SAR Image via Iterating Outliers and Recursing Saliency Depth
title_sort	target detection in high-resolution sar image via iterating outliers and recursing saliency depth
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/638c016a73c54f92b02445b8b2fafff5
work_keys_str_mv	AT zongyongcui targetdetectioninhighresolutionsarimageviaiteratingoutliersandrecursingsaliencydepth AT yiqin targetdetectioninhighresolutionsarimageviaiteratingoutliersandrecursingsaliencydepth AT yatingzhong targetdetectioninhighresolutionsarimageviaiteratingoutliersandrecursingsaliencydepth AT zongjiecao targetdetectioninhighresolutionsarimageviaiteratingoutliersandrecursingsaliencydepth AT haiyiyang targetdetectioninhighresolutionsarimageviaiteratingoutliersandrecursingsaliencydepth
_version_	1718431704986681344

Target Detection in High-Resolution SAR Image via Iterating Outliers and Recursing Saliency Depth

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