A Man-Made Target Extraction Method Based on Scattering Characteristics Using Multiaspect SAR Data

Multiaspect synthetic aperture radar (SAR) can obtain the response of the target to the radar illumination in the angle dimension. The radar cross section data referring to scattering behavior can be obtained by multiaspect SAR. The anisotropy characteristics of the man-made target in the imaging sc...

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Autores principales:	Xiaoyang Yue, Fei Teng, Yun Lin, Wen Hong
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Anisotropy distribution dual-channel man-made membership degree (MD) multiaspect Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/fca90fdb4b1e4c31bad9f25aa4a6dcf5
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id	oai:doaj.org-article:fca90fdb4b1e4c31bad9f25aa4a6dcf5
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spelling	oai:doaj.org-article:fca90fdb4b1e4c31bad9f25aa4a6dcf52021-12-02T00:00:04ZA Man-Made Target Extraction Method Based on Scattering Characteristics Using Multiaspect SAR Data2151-153510.1109/JSTARS.2021.3127537https://doaj.org/article/fca90fdb4b1e4c31bad9f25aa4a6dcf52021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9613769/https://doaj.org/toc/2151-1535Multiaspect synthetic aperture radar (SAR) can obtain the response of the target to the radar illumination in the angle dimension. The radar cross section data referring to scattering behavior can be obtained by multiaspect SAR. The anisotropy characteristics of the man-made target in the imaging scene can reflect its scattering changing behavior. Meanwhile, the amplitude characteristics of man-made target in some observing angles is target's another scattering property. Based on man-made target's anisotropy characteristics and amplitude characteristics, a dual-channel model (DCM) based on distribution and membership for man-made target extraction is generated in this article. The model considers man-made target's data changing in different observing aspects and strong points in specific aspects. First, the preprocessing of multiaspect data using low-rank matrix decomposition is discussed. Then, the parameters of SAR amplitude images’ distribution function are calculated by using expectation maximization method. Third, one channel is fuzzy C-means (FCM) method combining spatial neighborhood information for amplitude characteristics extraction. Another channel is statistical distribution model for anisotropy characteristics extraction. Finally, The calculated membership degree (MD) and statistical probability describe the man-made target and natural target. C-band circular SAR data is used to validate our method. The result of our DCM is compared with the result of only using a single channel model. The man-made targets are extracted better using the DCM.Xiaoyang YueFei TengYun LinWen HongIEEEarticleAnisotropydistributiondual-channelman-mademembership degree (MD)multiaspectOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11699-11712 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Anisotropy distribution dual-channel man-made membership degree (MD) multiaspect Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	Anisotropy distribution dual-channel man-made membership degree (MD) multiaspect Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Xiaoyang Yue Fei Teng Yun Lin Wen Hong A Man-Made Target Extraction Method Based on Scattering Characteristics Using Multiaspect SAR Data
description	Multiaspect synthetic aperture radar (SAR) can obtain the response of the target to the radar illumination in the angle dimension. The radar cross section data referring to scattering behavior can be obtained by multiaspect SAR. The anisotropy characteristics of the man-made target in the imaging scene can reflect its scattering changing behavior. Meanwhile, the amplitude characteristics of man-made target in some observing angles is target's another scattering property. Based on man-made target's anisotropy characteristics and amplitude characteristics, a dual-channel model (DCM) based on distribution and membership for man-made target extraction is generated in this article. The model considers man-made target's data changing in different observing aspects and strong points in specific aspects. First, the preprocessing of multiaspect data using low-rank matrix decomposition is discussed. Then, the parameters of SAR amplitude images’ distribution function are calculated by using expectation maximization method. Third, one channel is fuzzy C-means (FCM) method combining spatial neighborhood information for amplitude characteristics extraction. Another channel is statistical distribution model for anisotropy characteristics extraction. Finally, The calculated membership degree (MD) and statistical probability describe the man-made target and natural target. C-band circular SAR data is used to validate our method. The result of our DCM is compared with the result of only using a single channel model. The man-made targets are extracted better using the DCM.
format	article
author	Xiaoyang Yue Fei Teng Yun Lin Wen Hong
author_facet	Xiaoyang Yue Fei Teng Yun Lin Wen Hong
author_sort	Xiaoyang Yue
title	A Man-Made Target Extraction Method Based on Scattering Characteristics Using Multiaspect SAR Data
title_short	A Man-Made Target Extraction Method Based on Scattering Characteristics Using Multiaspect SAR Data
title_full	A Man-Made Target Extraction Method Based on Scattering Characteristics Using Multiaspect SAR Data
title_fullStr	A Man-Made Target Extraction Method Based on Scattering Characteristics Using Multiaspect SAR Data
title_full_unstemmed	A Man-Made Target Extraction Method Based on Scattering Characteristics Using Multiaspect SAR Data
title_sort	man-made target extraction method based on scattering characteristics using multiaspect sar data
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/fca90fdb4b1e4c31bad9f25aa4a6dcf5
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A Man-Made Target Extraction Method Based on Scattering Characteristics Using Multiaspect SAR Data

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