MLC-net: A sparse reconstruction network for TomoSAR imaging based on multi-label classification neural network

Abstract

Synthetic Aperture Radar tomography (TomoSAR) has garnered significant interest for its capability to achieve three-dimensional resolution along the elevation angle by collecting a stack of SAR images from different cross-track angles. Compressed Sensing (CS) algorithms have been widely introduced into SAR tomography. However, traditional CS-based TomoSAR methods suffer from weaknesses in noise resistance, high computational complexity, and insufficient super-resolution capabilities. Addressing the efficient TomoSAR imaging problem, this paper proposes an end-to-end neural network-based TomoSAR inversion method, named Multi-Label Classification-based Sparse Imaging Network (MLC-net). MLC-net focuses on the l0 norm optimization problem, completely departing from the iterative framework of traditional compressed sensing methods and overcoming the limitations imposed by the l1 norm optimization problem on signal coherence. Simultaneously, the concept of multi-label classification is introduced for the first time in TomoSAR inversion, enabling MLC-net to accurately invert scenarios with multiple scatterers within the same range-azimuth cell. Additionally, a novel evaluation system for TomoSAR inversion results is introduced, transforming inversion results into a 3D point cloud and utilizing mature evaluation methods for 3D point clouds. Under the new evaluation system, the proposed method is more than 30% stronger than existing methods. Finally, by training solely on simulated data, we conducted extensive experimental testing on both simulated and real data, achieving excellent results that validate the effectiveness, efficiency, and robustness of the proposed method. Specifically, the VQA_PC score improved from 91.085 to 92.713. The code of our network is available in https://github.com/OscarYoungDepend/MLC-net.