SAR Target Recognition via Joint Sparse Representation of Monogenic Signal

Abstract

In this paper, the classification via sprepresentation and multitask learning is presented for target recognition in SAR image. To capture the characteristics of SAR image, a multidimensional generalization of the analytic signal, namely the monogenic signal, is employed. The original signal can be then orthogonally decomposed into three components: 1) local amplitude; 2) local phase; and 3) local orientation. Since the components represent the different kinds of information, it is beneficial by jointly considering them in a unifying framework. However, these components are infeasible to be directly utilized due to the high dimension and redundancy. To solve the problem, an intuitive idea is to define an augmented feature vector by concatenating the components. This strategy usually produces some information loss. To cover the shortage, this paper considers three components into different learning tasks, in which some common information can be shared. Specifically, the component-specific feature descriptor for each monogenic component is produced first. Inspired by the recent success of multitask learning, the resulting features are then fed into a joint sparse representation model to exploit the intercorrelation among multiple tasks. The inference is reached in terms of the total reconstruction error accumulated from all tasks. The novelty of this paper includes 1) the development of three component-specific feature descriptors; 2) the introduction of multitask learning into sparse representation model; 3) the numerical implementation of proposed method; and 4) extensive comparative experimental studies on MSTAR SAR dataset, including target recognition under standard operating conditions, as well as extended operating conditions, and the capability of outliers rejection.

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.…”

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

“…Many classic classifiers have been used in SAR ATR, such as SVM [1], kNN [3], etc. The earlier mentioned dimensionality reduction methods PCA and LDA can also be taken as classifiers [4].…”

“…The earlier mentioned dimensionality reduction methods PCA and LDA can also be taken as classifiers [4]. Recently, the sparse representation classifier proposed by Wright [10] has been successfully applied to SAR applications, such as polarimetric SAR image classification [20] and SAR ATR [3]. The success of SRC is largely guaranteed by the high redundancy and low coherency of the dictionary atoms.…”

“…In the experiments under SOC, the images of ten targets acquired at a 17 • depression angle are used to train, while the images acquired at a 15 • depression angle are used to test [3,29]. The number of images for training and testing is tabulated in Table 1.…”

“…Although many works have been done in the past few decades [1][2][3][4][5][6][7][8], it is still a highly challenging problem. Generally, the process of SAR ATR includes four sequential stages: detection, discrimination, feature extraction and classification.…”