Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery

Infrared small target detection systems are an important part of space infrared imaging satellites. However, small infrared target detection is often affected by cirrus false alarm sources with similar grayscales. In this article, an infrared cirrus detection method based on the tensor robust princi...

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Autores principales:	Chunping Yang, Xuan Kong, Zhaoyang Cao, Zhenming Peng
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Tensor multi-mode expansion sum nuclear norm cirrus detection ADMM Ket augments wavelet transform Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/f2e1dafa159a4870b30d9f34b79d9f1a
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spelling	oai:doaj.org-article:f2e1dafa159a4870b30d9f34b79d9f1a2021-11-19T00:04:29ZCirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery2169-353610.1109/ACCESS.2020.3015975https://doaj.org/article/f2e1dafa159a4870b30d9f34b79d9f1a2020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9166504/https://doaj.org/toc/2169-3536Infrared small target detection systems are an important part of space infrared imaging satellites. However, small infrared target detection is often affected by cirrus false alarm sources with similar grayscales. In this article, an infrared cirrus detection method based on the tensor robust principal component analysis model (TRPCA) is proposed. The method treats multiple bands of remote sensing data as tensors, but classical tensor nuclear norms cannot represent the tensor rank well; therefore, we use tensor multi-mode expansion sum nuclear norm (TMESNN) to approximate the tensor rank better. First, a set of Landsat-8 data is transformed into a tensor model, and a TRPCA model is constructed by TMESNN and the <inline-formula> <tex-math notation="LaTeX">$L_{1} $ </tex-math></inline-formula> norm. Then, this model is solved by Ket augments and the alternating direction method of multipliers (ADMM). Finally, Mallat wavelet transform is used to supplement information and remove clutter, and the final detection result is obtained by adaptive threshold segmentation. Compared with other optimization-based methods, this method has better detection performance and accuracy.Chunping YangXuan KongZhaoyang CaoZhenming PengIEEEarticleTensor multi-mode expansion sum nuclear normcirrus detectionADMMKet augmentswavelet transformElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 149963-149983 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Tensor multi-mode expansion sum nuclear norm cirrus detection ADMM Ket augments wavelet transform Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Tensor multi-mode expansion sum nuclear norm cirrus detection ADMM Ket augments wavelet transform Electrical engineering. Electronics. Nuclear engineering TK1-9971 Chunping Yang Xuan Kong Zhaoyang Cao Zhenming Peng Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery
description	Infrared small target detection systems are an important part of space infrared imaging satellites. However, small infrared target detection is often affected by cirrus false alarm sources with similar grayscales. In this article, an infrared cirrus detection method based on the tensor robust principal component analysis model (TRPCA) is proposed. The method treats multiple bands of remote sensing data as tensors, but classical tensor nuclear norms cannot represent the tensor rank well; therefore, we use tensor multi-mode expansion sum nuclear norm (TMESNN) to approximate the tensor rank better. First, a set of Landsat-8 data is transformed into a tensor model, and a TRPCA model is constructed by TMESNN and the <inline-formula> <tex-math notation="LaTeX">$L_{1} $ </tex-math></inline-formula> norm. Then, this model is solved by Ket augments and the alternating direction method of multipliers (ADMM). Finally, Mallat wavelet transform is used to supplement information and remove clutter, and the final detection result is obtained by adaptive threshold segmentation. Compared with other optimization-based methods, this method has better detection performance and accuracy.
format	article
author	Chunping Yang Xuan Kong Zhaoyang Cao Zhenming Peng
author_facet	Chunping Yang Xuan Kong Zhaoyang Cao Zhenming Peng
author_sort	Chunping Yang
title	Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery
title_short	Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery
title_full	Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery
title_fullStr	Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery
title_full_unstemmed	Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery
title_sort	cirrus detection based on tensor multi-mode expansion sum nuclear norm in infrared imagery
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/f2e1dafa159a4870b30d9f34b79d9f1a
work_keys_str_mv	AT chunpingyang cirrusdetectionbasedontensormultimodeexpansionsumnuclearnormininfraredimagery AT xuankong cirrusdetectionbasedontensormultimodeexpansionsumnuclearnormininfraredimagery AT zhaoyangcao cirrusdetectionbasedontensormultimodeexpansionsumnuclearnormininfraredimagery AT zhenmingpeng cirrusdetectionbasedontensormultimodeexpansionsumnuclearnormininfraredimagery
_version_	1718420704322387968

Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery

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