Compact convolutional autoencoder for SAR target recognition

Abstract

Learning discriminative features is difficult for deep learning-based target recognition in synthetic aperture radar (SAR) images with small training samples. To achieve a better feature learning, this study proposes a new deep network, a compact convolutional autoencoder (CCAE) for SAR target recognition. CCAE minimises the reconstruction loss and the distance between intra-class samples simultaneously by imposing compactness constraint on the encoder, which results in a more discriminative feature representation. Furthermore, the pretrained CCAE encoder can be used to initialise the corresponding parameters of a convolutional neural network to facilitate the training of the end-to-end model. Experimental results using the moving and stationary target acquisition and recognition dataset show that the proposed method outperforms the existing deep learning-based methods in the case of small training samples.