Multiview Low-Rank Hybrid Dilated Network for SAR Target Recognition Using Limited Training Samples

Deep learning (DL) have been intensively exploited for synthetic aperture radar (SAR) target classification, and we have witnessed exciting performances provided by previous studies. Multiview-based DL methods exhibited huge potentials for SAR target classification since they can generate adequate sample images from few raw images. In order to further improve the generalization performance of the multiview-based framework with limited training samples, we propose a novel multiview low-rank hybrid dilated network (MLHDN) for SAR target recognition. Firstly, we design a parameter-sharing hybrid dilated convolution (HDC) to learn multiview features. Secondly, a composite low-rank bilinear pooling (CLRBP) is proposed to fuse multiview features and to reduce their dimensions, yielding class-oriented and compacted feature vectors which are distinctive and representative for classification. Finally, a Softmax layer is used as a classifier. Accordingly, MLHDN encompasses fewer parameters compared to existing multiview-based DL methods. Experimental results demonstrate that MLHDN can achieve state-of-the-art performances on the moving and stationary target acquisition and recognition (MSTAR) datasets for SAR target classification, yielding an accuracy of 96.13% with only 10 training samples per class.