Recognition of targets in SAR images using joint classification of deep features fused by multi-canonical correlation analysis

Abstract

ABSTRACTThis letter proposes a synthetic aperture radar (SAR) target recognition method via joint classification of deep features fused by multi-canonical correlation analysis (MCCA). A convolutional neural network (CNN) is designed for feature learning from original SAR images. For the multiple feature maps from different convolution layers, they are fused based on the MCCA to maintain the relevance while eliminating the redundancy. Afterwards, the joint sparse representation (JSR) is employed to jointly represent the fused deep feature vectors from different convolution layers under the constraint of their inner correlations. Based on the reconstruction errors from JSR, the target label can be classified. The proposed method can make full use of the multi-level deep features by using the correlations among the same layer and between different layers. Experiments are investigated on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set and the results confirm the performance of the proposed method.