Combination of Joint Representation and Adaptive Weighting for Multiple Features with Application to SAR Target Recognition

Abstract

For the synthetic aperture radar (SAR) target recognition problem, a method combining multifeature joint classification and adaptive weighting is proposed with innovations in fusion strategies. Zernike moments, nonnegative matrix factorization (NMF), and monogenic signal are employed as the feature extraction algorithms to describe the characteristics of original SAR images with three corresponding feature vectors. Based on the joint sparse representation model, the three types of features are jointly represented. For the reconstruction error vectors from different features, an adaptive weighting algorithm is used for decision fusion. That is, the weights are adaptively obtained under the framework of linear fusion to achieve a good fusion result. Finally, the target label is determined according to the fused error vector. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset under the standard operating condition (SOC) and four extended operating conditions (EOC), i.e., configuration variants, depression angle variances, noise interference, and partial occlusion. The results verify the effectiveness and robustness of the proposed method.

Automatic Target Recognition of SAR Images Using Collaborative Representation

“…Sparse representation is essentially a linear representation theory [ 22 – 27 ]. Different from the general linear representation, the sparse representation requires the linear representation coefficients to be sparse, that is, only a small number of coefficients are nonzero.…”

“…The classifier design aims to make decisions on the original SAR image or the extracted features to determine the target category. With the advancement of pattern recognition technology, a large number of classifiers have been applied in SAR target recognition, such as K-nearest neighbor (KNN) [ 8 ], support vector machine (SVM) [ 19 – 21 ], sparse representation-based classification (SRC) [ 22 – 27 ], convolutional neural network (CNN) [ 28 – 39 ], and so on. Mishra applied PCA and LDA to SAR image feature extraction and classified them through KNN [ 11 ].…”

SAR Target Recognition via Monogenic Signal and Gaussian Process Model

“…At this stage, most of the classi ers used in SAR target recognition came from the traditional pattern recognition eld. Representative ones are K nearest neighbors (KNN) [9,20], support vector machine (SVM) [20][21][22][23], sparse representation-based classi cation (SRC) [23][24][25][26][27][28][29] etc. In recent years, traditional neural networks have gradually moved to a deeper level, promoting the rapid development of deep learning theory.…”

Target Recognition of SAR Images Based on SVM and KSRC

“…The classifiers design appropriate classification strategies for the extracted features to output the target label of the test sample. At present, a rich set of classifiers are available in SAR target recognition, including the K nearest neighbor (KNN) classifier [ 7 ], support vector machine (SVM) [ 19 – 21 ], and sparse representation-based classification (SRC) [ 22 – 27 ]. Recently, many SAR target recognition methods were developed based on the deep learning tools, among which the convolutional neural network (CNN) is a typical representative [ 28 – 35 ].…”