Component Interpretation for SAR Target Images Based on Deep Generative Model

Abstract

A fast and precise interpretation of SAR images is an important and challenging research topic. Some progress has been made in optical image interpretation through decoupling analysis method, while research on decoupling components of SAR images is still in a blank stage. To make an initial exploration on the component interpretation of SAR target images, we propose a new network based on a deep generative model and a new decoupling method. Due to the lack of real training samples that meet the required condition, we use electromagnetic simulation software FEKO to construct the training data sets. In our proposed method, we use the tag information of training samples to constrain the hidden variable layer and improve the structure and loss function of the residual variation autoencoder (Res-VAE) network. By optimizing the newly defined loss function, the network can get the decipherable component features and achieve component interpretation of SAR images. Our experiments verify the feasibility and practicability of the proposed network through the simulation data sets and MSTAR data sets. The results show that the proposed method is effective in interpreting the target components of SAR images.