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Remote Sens. 2017, 9(5), 482; doi:10.3390/rs9050482

Hyperspectral Target Detection via Adaptive Joint Sparse Representation and Multi-Task Learning with Locality Information

1
Hubei Subsurface Multi-Scale Imaging Key Laboratory, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
2
School of Computer, Wuhan University, Wuhan 430079, China
3
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez, Xiaofeng Li and Prasad S. Thenkabail
Received: 16 April 2017 / Revised: 7 May 2017 / Accepted: 12 May 2017 / Published: 14 May 2017
(This article belongs to the Collection Learning to Understand Remote Sensing Images)
View Full-Text   |   Download PDF [5825 KB, uploaded 15 May 2017]   |  

Abstract

Target detection from hyperspectral images is an important problem but encounters a critical challenge of simultaneously reducing spectral redundancy and preserving the discriminative information. Recently, the joint sparse representation and multi-task learning (JSR-MTL) approach was proposed to address the challenge. However, it does not fully explore the prior class label information of the training samples and the difference between the target dictionary and background dictionary when constructing the model. Besides, there may exist estimation bias for the unknown coefficient matrix with the use of minimization which is usually inconsistent in variable selection. To address these problems, this paper proposes an adaptive joint sparse representation and multi-task learning detector with locality information (JSRMTL-ALI). The proposed method has the following capabilities: (1) it takes full advantage of the prior class label information to construct an adaptive joint sparse representation and multi-task learning model; (2) it explores the great difference between the target dictionary and background dictionary with different regularization strategies in order to better encode the task relatedness; (3) it applies locality information by imposing an iterative weight on the coefficient matrix in order to reduce the estimation bias. Extensive experiments were carried out on three hyperspectral images, and it was found that JSRMTL-ALI generally shows a better detection performance than the other target detection methods. View Full-Text
Keywords: hyperspectral image; target detection; multi-task learning; sparse representation; locality information hyperspectral image; target detection; multi-task learning; sparse representation; locality information
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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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Zhang, Y.; Wu, K.; Du, B.; Zhang, L.; Hu, X. Hyperspectral Target Detection via Adaptive Joint Sparse Representation and Multi-Task Learning with Locality Information. Remote Sens. 2017, 9, 482.

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