EMI-Net: An End-to-End Mechanism-Driven Interpretable Network for SAR Target Recognition Under EOCs
Leiyao Liao, Lan Du, Jian Chen, Zhuowei Cao, Ke’er Zhou
Abstract
Most existing synthetic aperture radar (SAR) target recognition methods based on deep learning are of black boxes structure and data-drive networks, which are faced with the issue of severe performance degradation under extended operating conditions (EOCs). To address the issue, this paper proposes an end-to-end mechanism-driven interpretable network (EMI-Net) for SAR target recognition under EOCs. The EMI-Net achieves the integration of scattering center feature extraction and target recognition in an end-to-end framework to avoid the mismatch between scattering center features with classifier and also explore the representative electromagnetic characteristics that are useful for recognition under EOCs. In EMI-Net, by unfolding a sparse solving algorithm and integrating scattering center model into deep networks, our model contains feature encoding and decoding procedures of SAR images. Thus, EMI-Net is an interpretable deep unfolding network that is driven by physical mechanism to precisely learn scattering center features reflecting locations and amplitude of SAR targets based on the deep learning mode. Our EMI-Net divides input images into multiple patches based on the image-domain scattering center model to reduce its computation and space complexities. In addition, EMI-Net treats the scattering centers as discrete three-dimensional point data and designs a point cloud network as classifier to explore permutation-invariant representations for recognition. Results on the measured dataset validate that EMI-Net gains superior scattering center extraction and target recognition results under EOCs, and also shows high time-efficiency and low memory requirement.