Hyperspectral anomaly detection via low-rank and sparse decomposition with cluster subspace accumulation

Abstract Anomaly detection (AD) has emerged as a prominent area of research in hyperspectral imagery (HSI) processing. Traditional algorithms, such as low-rank and sparse matrix decomposition (LRaSMD), often struggle to effectively address challenges related to background interference, anomaly targe...

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Главные авторы:	Baozhi Cheng, Yan Gao
Формат:	Artigo
Язык:	Inglês
Опубликовано:	Nature Portfolio 2024-11-01
Серии:	Scientific Reports
Предметы:	Hyperspectral imagery (HSI) Anomaly target detection Low-rank and sparse decomposition Cluster subspace
Online-ссылка:	https://doi.org/10.1038/s41598-024-80137-3
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