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...

Full description

Bibliographic Details

Published in:	Scientific Reports
Main Authors:	Baozhi Cheng, Yan Gao
Format:	Article
Language:	English
Published:	Nature Portfolio 2024-11-01
Subjects:	Hyperspectral imagery (HSI) Anomaly target detection Low-rank and sparse decomposition Cluster subspace
Online Access:	https://doi.org/10.1038/s41598-024-80137-3