Two-Stage Multi-Task Representation Learning for Synthetic Aperture Radar (SAR) Target Images Classification

Sensors

Yongming Li¹  Shujun Liu¹  Yijian Wang¹  Xinzheng Zhang¹  Zhiying Tan¹  Tao Wang²  Dong Li² 

[1] College of Communication Engineering, Chongqing University, Chongqing 400044, China;Key Laboratory of Aerocraft Tracking Telementering & Command and Communication, Chongqing University, Chongqing 400044, China;

关键词: SAR;    images classification;    multitask learning;    sparse representation;    collaborative representation;   

DOI  :  10.3390/s17112506

来源: DOAJ

【 摘 要 】

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 aℓ2 , 1-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.

【 授权许可】

Unknown