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Anomaly Detection in Hyperspectral Imagery Based on Low-Rank Representation Incorporating a Spatial Constraint

1,2,3,†, 3,†, 3, 3,* and 4
1
Key Laboratory of Geographic Information (Ministry of Education), East China Normal University, Shanghai 200241, China
2
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
3
Key Laboratory for Land Environment and Disaster Monitoring of NASG, China University of Minning and Technology, Xuzhou 221116, China
4
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2019, 11(13), 1578; https://doi.org/10.3390/rs11131578
Received: 7 May 2019 / Revised: 20 June 2019 / Accepted: 27 June 2019 / Published: 3 July 2019
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Abstract

Hyperspectral imagery contains abundant spectral information. Each band contains some specific characteristics closely related to target objects. Therefore, using these characteristics, hyperspectral imagery can be used for anomaly detection. Recently, with the development of compressed sensing, low-rank-representation-based methods have been applied to hyperspectral anomaly detection. In this study, novel low-rank representation methods were developed for anomaly detection from hyperspectral images based on the assumption that hyperspectral pixels can be effectively decomposed into a low-rank component (for background) and a sparse component (for anomalies). In order to improve detection performance, we imposed a spatial constraint on the low-rank representation coefficients, and single or multiple local window strategies was applied to smooth the coefficients. Experiments on both simulated and real hyperspectral datasets demonstrated that the proposed approaches can effectively improve hyperspectral anomaly detection performance. View Full-Text
Keywords: anomaly detection; hyperspectral; low-rank representation; local window; spatial constraint anomaly detection; hyperspectral; low-rank representation; local window; spatial constraint
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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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Tan, K.; Hou, Z.; Ma, D.; Chen, Y.; Du, Q. Anomaly Detection in Hyperspectral Imagery Based on Low-Rank Representation Incorporating a Spatial Constraint. Remote Sens. 2019, 11, 1578.

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