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

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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Bibliographic Details
Main Authors: Baozhi Cheng, Yan Gao
Format: Article
Language:English
Published: Nature Portfolio 2024-11-01
Series:Scientific Reports
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

Internet

https://doi.org/10.1038/s41598-024-80137-3

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