Compressive sensing-based SAR imaging method for remote sensing

A compressive sensing-based synthetic aperture radar imaging method is investigated to address the challenges of high-resolution imaging and massive data processing in spaceborne remote sensing. High-fidelity recovery of SAR images from sub-Nyquist sampled data is achieved by combining data-driven overcomplete dictionary, structured measurement matrix, and sparse reconstruction optimization methods. Numerical simulations are conducted by SAR dataset of different scenes, and the illustration shows that the method maintains high-quality image reconstruction over a range of compression ratios from 20% to 80%. The experiment results indicates that the method can maintain high image quality with low data volume. It provides a feasible solution for the application of CS in spaceborne SAR imaging system and promotes the practical development of remote sensing imaging technology.