SAR Target Recognition Based on Enhanced Discriminant Feature Learning

Konferenz: EUSAR 2021 - 13th European Conference on Synthetic Aperture Radar
29.03.2021 - 01.04.2021 in online

Tagungsband: EUSAR 2021

Seiten: 5Sprache: EnglischTyp: PDF

Guo, Jun; Wang, Ling; Zhu, Daiyin; Hu, Changyu (Key Laboratory of Radar Imaging and Microwave Photonics of the Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, China)

This paper proposes a SAR target recognition method based on enhanced discriminant feature learning. Specifically, a compactness constraint is added to the convolutional autoencoder (CAE) to minimizes the reconstruction loss and the intra-class sample distance, which results in a enhanced discriminant feature representation. Afterwards, we use the encoder of the pretrained CAE with compactness constraint to initialize a convolutional neural network (CNN) to construct an end-to-end recognition model. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset show that the proposed method achieves competitive performance with limited training samples.