A Randomized Subspace Learning Based Anomaly Detector for Hyperspectral Imagery

A Randomized Subspace Learning Based Anomaly Detector for Hyperspectral Imagery

This paper proposes a randomized subspace learning based anomaly detector (RSLAD) for hyperspectral imagery (HSI). Improved from robust principal component analysis, the RSLAD assumes that the background matrix is low-rank, and the anomaly matrix is sparse with a small portion of nonzero columns (i....

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Bibliographic Details
Main Authors: Weiwei Sun, Long Tian, Yan Xu, Bo Du, Qian Du
Format: Article
Language:English
Published: MDPI AG 2018-03-01
Series:Remote Sensing
Subjects:
low-rank recovery
randomized subspace learning
anomaly detection
hyperspectral imagery
Online Access:http://www.mdpi.com/2072-4292/10/3/417

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