Novel Loss Function in CNN for Small Sample Target Recognition in SAR Images

Abstract

Studying synthetic aperture radar automatic target recognition (SAR-ATR) under small samples can get rid of the sample dependence and improve the practicality of the deep learning model. However, the deep learning model trained with small samples is prone to overfitting. In order to solve the above problem of SAR-ATR, a novel loss function called limited data loss function (LDLF) is proposed in this letter, which organically combines the cross-entropy loss function and the contrastive loss function. The LDLF supervises the convolutional neural network (CNN) to learn strong generalization performance features. Then, for simplifying the training and testing of CNN based on LDLF, a feature-combined module is proposed. This module makes up for the limitation that the model input must be image pairs and simplifies the testing process of CNN. Experiments on moving and stationary target acquisition and recognition (MSTAR) datasets show that the proposed loss function is better than the cross-entropy loss function and superior to the existing methods in synthetic aperture radar (SAR) image target recognition using small samples data.