Hyperspectral Anomaly Detection Based on Low-Rank Representation and Learned Dictionary

Hyperspectral Anomaly Detection Based on Low-Rank Representation and Learned Dictionary

In this paper, a novel hyperspectral anomaly detector based on low-rank representation (LRR) and learned dictionary (LD) has been proposed. This method assumes that a two-dimensional matrix transformed from a three-dimensional hyperspectral imagery can be decomposed into two parts: a low rank matrix...

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Bibliographic Details
Main Authors: Yubin Niu, Bin Wang
Format: Article
Language:English
Published: MDPI AG 2016-03-01
Series:Remote Sensing
Subjects:
hyperspectral imagery
anomaly detection
low-rank matrix decomposition
learned dictionary
robust PCA
low-rank representation
Online Access:http://www.mdpi.com/2072-4292/8/4/289

Internet

http://www.mdpi.com/2072-4292/8/4/289

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