SAR Target Recognition via Supervised Discriminative Dictionary Learning and Sparse Representation of the SAR-HOG Feature

Abstract

Automatic target recognition (ATR) in synthetic aperture radar (SAR) images plays an important role in both national defense and civil applications. Although many methods have been proposed, SAR ATR is still very challenging due to the complex application environment. Feature extraction and classification are key points in SAR ATR. In this paper, we first design a novel feature, which is a histogram of oriented gradients (HOG)-like feature for SAR ATR (called SAR-HOG). Then, we propose a supervised discriminative dictionary learning (SDDL) method to learn a discriminative dictionary for SAR ATR and propose a strategy to simplify the optimization problem. Finally, we propose a SAR ATR classifier based on SDDL and sparse representation (called SDDLSR), in which both the reconstruction error and the classification error are considered. Extensive experiments are performed on the MSTAR database under standard operating conditions and extended operating conditions. The experimental results show that SAR-HOG can reliably capture the structures of targets in SAR images, and SDDL can further capture subtle differences among the different classes. By virtue of the SAR-HOG feature and SDDLSR, the proposed method achieves the state-of-the-art performance on MSTAR database. Especially for the extended operating conditions (EOC) scenario "Training 17 • -Testing 45 • ", the proposed method improves remarkably with respect to the previous works.

Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data

“…It is 8.67%, 5.43%, 5.68%, 2.85%, 4.09% better than SVM, sparse representation of monogenic signal (MSRC) [18], tri-task joint sparse representation (TJSR) [48], supervised discriminative dictionary learning and sparse representation (SDDLSR) [8] and joint dynamic sparse representation (JDSR) [49]. In addition, our method can achieve a comparable performance to the state-of-the-art methods based on deep learning (A-ConvNet [24] and DCHUN [50]), shown in Figure 12.…”

“…On the one hand, various types of classifiers, such as sparse representation-based method [2], Bayes classifier [3], mean square error (MSE) classifier [4], template-based classifiers [5] and support vector machine (SVM) [6], are selected to solve the problem. Among them, the sparse representation classification is popular in recent studies [7,8]. The improved joint sparse representation model was proposed to effectively combine multiple-view SAR images from the same physical target [9].…”

A Sparse SAR Imaging Method Based on Multiple Measurement Vectors Model

“…Since 2006 [31], plenty of recovery algorithms have been developed for solving the MMV problems [32,33], such as the Focal Undetermined System Solver method [34] and the Basis Pursuit method [35,36] They can be divided into two categories, the convex optimization and the greedy method. In this paper, one of the greedy algorithms, OMP (Orthogonal Matching Pursuit), is discussed because of its robustness and simplicity.…”

SAR Image Recognition with Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse Representation

“…The resurgent development of many theoretical analysis frameworks [22,23] and effective algorithms [24] has been witnessed. The applications of the sparse signal representation technique mainly include radar imaging [25,26], image restoration [27], image classification [28,29], and pattern recognition [15,30]. The key of sparse signal model is based on the fact that a certain signal can be represented by an overcomplete basis set (dictionary).…”