ASC-Parts Model Guided Multi-Level Fusion Network for SAR Target Classification

Abstract

Most deep-learning based synthetic aperture radar (SAR) target classification methods directly apply models designed for natural scenes and do not consider the difference between SAR and optical images. To solve this problem, a parts model guided multi-level fusion network for synthetic aperture radar (SAR) target classification is proposed in this paper, which integrates the electromagnetic scattering features into the deep learning network. Firstly, attribute scattering center (ASC) model based target parts (ASC-Parts) are extracted, which provide scattering features of target from physical model. Then, under the guidance of the ASC-Parts model, the local features of target are further extracted based on the proposed SAR target parts segmentation network. Through this physics guided network, we can introduce the target scattering characteristics into deep learning. Finally, a multi-level fusion structure is developed, which adds the local features to the global features obtained from different layers of traditional deep learning network. The experimental results on Moving and Stationary Target Acquisition and Recognition (MSTAR) data set show the superiority of the novel method and illustrate the effectiveness of physics guided deep learning for SAR target classification.