SAR Automatic Target Recognition Based on Multi-Scale Convolutional Factor Analysis Model with Max-Margin Constraint

In this study, a multi-scale convolutional factor analysis model with max-margin constraint (MMCFA) is developed for synthetic aperture radar (SAR) automatic target recognition. Compared with the traditional factor analysis (FA) model, the CFA model can maintain the spatial correlation among the image pixels in two-dimensional space and capture the structural information from images via convolution kernels. Moreover, multi-scale convolution kernels are adopted to capture richer features at different scales of SAR. Since the unsupervised model may not offer discriminative factors for the SAR target recognition task, it is expected to introduce the supervised information to the multi-scale CFA model when supervised information is available. Thus, a latent variable support vector machine (LVSVM) is linked to the factors learned from multi-scale CFA model, yielding max-margin discrimination, learned with the multi-scale CFA model jointly in a united framework. Experimental results on MSTAR dataset show that the proposed model has excellent recognition performance.