Abstract
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.
Highlights
IntroductionAutomatic target-recognition systems using Synthetic aperture radar (SAR) sensors continue to be developed for a number of applications, in the area of military defense
Synthetic aperture radar (SAR) are widely applied in various civil and military fields, such as aerial remote sensing to detect targets [1], environmental monitoring [2], and maritime surveillance [3].Automatic target-recognition systems using SAR sensors continue to be developed for a number of applications, in the area of military defense
2017, 17, 2506 multi-task joint sparse in the first stage, which can greatly reduce the interference2,1 -norm of irrelevant atoms. This agrees with the characteristics of SAR target images, which are sensitive
Summary
Automatic target-recognition systems using SAR sensors continue to be developed for a number of applications, in the area of military defense. The goal of these systems is to detect and classify military targets using various image- and signal-processing techniques. General reviews of automatic target-recognition concepts and the SAR target-detection technologies can be found in [4,5]. This paper focuses on the final classification stage of the SAR automatic target-classification system. Target images obtained by SAR are significantly different with target optical images because microwave imaging is based on a scattering mechanism. The characteristics of SAR target images are very sensitive to azimuth and elevation
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