A Multiview Inter-class Dissimilarity Feature Fusion SAR Images Recognition Network within Limited Sample Condition

In Synthetic aperture radar (SAR) recognition tasks, due to its special imaging principle, SAR images acquired from different viewpoints contain target features that may carry a large amount of information. These target features from different viewpoints enrich the training data and also improve the target recognition performance. However, considering the relative difficulty of acquiring SAR images and their fine annotations, if recognition is forced by fusion of multiview features when raw data is scarce, feature redundancy will be formed, which in turn will lead to a decrease in recognition accuracy. To remedy the above problem, a multiview inter-class dissimilarity feature fusion (MIDFF) network is proposed in this study. The proposed network can simultaneously input images of two classes, one of which is a multiview input used to extract features from the same target at different views, and the other is input from other classes, which we call Input-heterogeneous, used to extract heterogeneous features. Firstly, a method for rapidly generating sufficient training data for MIDFF is proposed, which generates training data by repeatedly combining images from different views and classes to ensure that a large number of training inputs are available even when raw SAR images are scarce. Secondly, a method of calculating and enhancing of inter-class dissimilarity (ICD) features is proposed to increase the inter-class distance and improve the inter-class separability. Then, the ICD and multiview features are fused to increase the features learned by the network and reduce feature redundancy. Finally, a multiview heterogeneous weighted loss function is proposed, which combines the calculation of inter-class similarity and heterogeneous loss. Through the gradual convergence of the loss function, the inter-class similarity decreases as the loss decreases, which further improves the target recognition performance. Experimental results on MSTAR and Civilian Vehicle SAR datasets show that our proposed method performs better than the state-of-the-art methods within sample scarcity conditions.