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Weighted Sparseness-Based Anomaly Detection for Hyperspectral Imagery

Anomaly detection of hyperspectral remote sensing data has recently become more attractive in hyperspectral image processing. The low-rank and sparse matrix decomposition-based anomaly detection algorithm (LRaSMD) exhibits poor detection performance in complex scenes with multiple background edges a...

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Autores principales: Lian, Xing, Zhao, Erwei, Zheng, Wei, Peng, Xiaodong, Li, Ang, Zhen, Zheng, Wen, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9959882/
https://www.ncbi.nlm.nih.gov/pubmed/36850660
http://dx.doi.org/10.3390/s23042055
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author Lian, Xing
Zhao, Erwei
Zheng, Wei
Peng, Xiaodong
Li, Ang
Zhen, Zheng
Wen, Yan
author_facet Lian, Xing
Zhao, Erwei
Zheng, Wei
Peng, Xiaodong
Li, Ang
Zhen, Zheng
Wen, Yan
author_sort Lian, Xing
collection PubMed
description Anomaly detection of hyperspectral remote sensing data has recently become more attractive in hyperspectral image processing. The low-rank and sparse matrix decomposition-based anomaly detection algorithm (LRaSMD) exhibits poor detection performance in complex scenes with multiple background edges and noise. Therefore, this study proposes a weighted sparse hyperspectral anomaly detection method. First, using the idea of matrix decomposition in mathematics, the original hyperspectral data matrix is reconstructed into three sub-matrices with low rank, small sparsity and representing noise, respectively. Second, to suppress the noise interference in the complex background, we employed the low-rank, background image as a reference, built a local spectral and spatial dictionary through the sliding window strategy, reconstructed the HSI pixels of the original data, and extracted the sparse coefficient. We proposed the sparse coefficient divergence evaluation index (SCDI) as a weighting factor to weight the sparse anomaly map to obtain a significant anomaly map to suppress the background edge, noise, and other residues caused by decomposition, and enhance the abnormal target. Finally, abnormal pixels are segmented based on the adaptive threshold. The experimental results demonstrate that, on a real-scene hyperspectral dataset with a complicated background, the proposed method outperforms the existing representative algorithms in terms of detection performance.
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spelling pubmed-99598822023-02-26 Weighted Sparseness-Based Anomaly Detection for Hyperspectral Imagery Lian, Xing Zhao, Erwei Zheng, Wei Peng, Xiaodong Li, Ang Zhen, Zheng Wen, Yan Sensors (Basel) Article Anomaly detection of hyperspectral remote sensing data has recently become more attractive in hyperspectral image processing. The low-rank and sparse matrix decomposition-based anomaly detection algorithm (LRaSMD) exhibits poor detection performance in complex scenes with multiple background edges and noise. Therefore, this study proposes a weighted sparse hyperspectral anomaly detection method. First, using the idea of matrix decomposition in mathematics, the original hyperspectral data matrix is reconstructed into three sub-matrices with low rank, small sparsity and representing noise, respectively. Second, to suppress the noise interference in the complex background, we employed the low-rank, background image as a reference, built a local spectral and spatial dictionary through the sliding window strategy, reconstructed the HSI pixels of the original data, and extracted the sparse coefficient. We proposed the sparse coefficient divergence evaluation index (SCDI) as a weighting factor to weight the sparse anomaly map to obtain a significant anomaly map to suppress the background edge, noise, and other residues caused by decomposition, and enhance the abnormal target. Finally, abnormal pixels are segmented based on the adaptive threshold. The experimental results demonstrate that, on a real-scene hyperspectral dataset with a complicated background, the proposed method outperforms the existing representative algorithms in terms of detection performance. MDPI 2023-02-11 /pmc/articles/PMC9959882/ /pubmed/36850660 http://dx.doi.org/10.3390/s23042055 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lian, Xing
Zhao, Erwei
Zheng, Wei
Peng, Xiaodong
Li, Ang
Zhen, Zheng
Wen, Yan
Weighted Sparseness-Based Anomaly Detection for Hyperspectral Imagery
title Weighted Sparseness-Based Anomaly Detection for Hyperspectral Imagery
title_full Weighted Sparseness-Based Anomaly Detection for Hyperspectral Imagery
title_fullStr Weighted Sparseness-Based Anomaly Detection for Hyperspectral Imagery
title_full_unstemmed Weighted Sparseness-Based Anomaly Detection for Hyperspectral Imagery
title_short Weighted Sparseness-Based Anomaly Detection for Hyperspectral Imagery
title_sort weighted sparseness-based anomaly detection for hyperspectral imagery
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9959882/
https://www.ncbi.nlm.nih.gov/pubmed/36850660
http://dx.doi.org/10.3390/s23042055
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