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

机译：合成孔径雷达（SAR）目标图像分类的两阶段多任务表示学习

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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 a ℓ2,1-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.
机译：在本文中，我们提出了一种用于合成孔径雷达目标图像分类的两阶段多任务学习表示方法。拟议方法的第一阶段使用多特征联合稀疏表示学习（建模为ℓ2,1-范数正则化多任务稀疏学习问题），以找到有效的训练样本子集。然后，基于训练子集构建新词典。该方法的第二阶段是利用多任务协作表示，基于新字典执行目标图像分类。该算法不仅具有多种特征的识别能力，而且还大大降低了与测试样品无关的原子的干扰，从而有效地提高了分类性能。通过移动和固定目标获取与识别（MSTAR）公共SAR数据库进行的实验结果表明，该方法有效且优于许多最新方法。

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