A Tensor-Based Go Decomposition Method for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection (HAD) aims at effectively separating the anomaly target from the background. The low-rank and sparse matrix decomposition (LRaSMD) technique has shown great potential in HAD tasks. However, some LRaSMD models need to convert the hyperspectral data into a two-dimension...

Full description

Bibliographic Details

Published in:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Main Authors:	Meiping Song, Xiao Zhang, Lan Li, Hongju Cao, Haimo Bao
Format:	Article
Language:	English
Published:	IEEE 2025-01-01
Subjects:	Go decomposition hyperspectral anomaly detection (HAD) low rank and sparse matrix decomposition (LRaSMD) singular value threshold tensor representation
Online Access:	https://ieeexplore.ieee.org/document/10836889/