Two-Stage Multi-Task Representation Learning for Synthetic Aperture Radar (SAR) Target Images Classification
College of Communication Engineering, Chongqing University, Chongqing 400044, China
Key Laboratory of Aerocraft Tracking Telementering & Command and Communication, Chongqing University, Chongqing 400044, China
Author to whom correspondence should be addressed.
Received: 24 August 2017 / Revised: 4 October 2017 / Accepted: 28 October 2017 / Published: 1 November 2017
In this paper, we propose a two-stage multi-task learning representation method for the classification of synthetic aperture radar (SAR) target images. The first stage of the proposed approach uses multi-features joint sparse representation learning, modeled as a
-norm regularized multi-task sparse learning problem, to find an effective subset of training samples. Then, a new dictionary is constructed based on the training subset. The second stage of the method is to perform target images classification based on the new dictionary, utilizing multi-task collaborative representation. The proposed algorithm not only exploits the discrimination ability of multiple features but also greatly reduces the interference of atoms that are irrelevant to the test sample, thus effectively improving classification performance. Conducted with the Moving and Stationary Target Acquisition and Recognition (MSTAR) public SAR database, experimental results show that the proposed approach is effective and superior to many state-of-the-art methods.
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Zhang, X.; Wang, Y.; Tan, Z.; Li, D.; Liu, S.; Wang, T.; Li, Y. Two-Stage Multi-Task Representation Learning for Synthetic Aperture Radar (SAR) Target Images Classification. Sensors 2017, 17, 2506.
Zhang X, Wang Y, Tan Z, Li D, Liu S, Wang T, Li Y. Two-Stage Multi-Task Representation Learning for Synthetic Aperture Radar (SAR) Target Images Classification. Sensors. 2017; 17(11):2506.
Zhang, Xinzheng; Wang, Yijian; Tan, Zhiying; Li, Dong; Liu, Shujun; Wang, Tao; Li, Yongming. 2017. "Two-Stage Multi-Task Representation Learning for Synthetic Aperture Radar (SAR) Target Images Classification." Sensors 17, no. 11: 2506.
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