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Remote Sens. 2016, 8(4), 289; doi:10.3390/rs8040289

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

1,2,3
and
1,2,3,*
1
Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, China
2
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
3
Research Center of Smart Networks and Systems, School of Information Science and Technology, Fudan University, Shanghai 200433, China
*
Author to whom correspondence should be addressed.
Academic Editors: Magaly Koch and Prasad S. Thenkabail
Received: 17 December 2015 / Revised: 15 March 2016 / Accepted: 22 March 2016 / Published: 28 March 2016
View Full-Text   |   Download PDF [5673 KB, uploaded 28 March 2016]   |  

Abstract

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 representing the background and a sparse matrix standing for the anomalies. The direct application of LRR model is sensitive to a tradeoff parameter that balances the two parts. To mitigate this problem, a learned dictionary is introduced into the decomposition process. The dictionary is learned from the whole image with a random selection process and therefore can be viewed as the spectra of the background only. It also requires a less computational cost with the learned dictionary. The statistic characteristic of the sparse matrix allows the application of basic anomaly detection method to obtain detection results. Experimental results demonstrate that, compared to other anomaly detection methods, the proposed method based on LRR and LD shows its robustness and has a satisfactory anomaly detection result. View Full-Text
Keywords: hyperspectral imagery; anomaly detection; low-rank matrix decomposition; learned dictionary; robust PCA; low-rank representation hyperspectral imagery; anomaly detection; low-rank matrix decomposition; learned dictionary; robust PCA; low-rank representation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Niu, Y.; Wang, B. Hyperspectral Anomaly Detection Based on Low-Rank Representation and Learned Dictionary. Remote Sens. 2016, 8, 289.

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