Multi-Parameter Regularization Method for Synthetic Aperture Imaging Radiometers

Synthetic aperture imaging radiometers (SAIRs) are powerful passive microwave systems for high-resolution by use of synthetic technique. However, the ill-posed inverse problem SAIRs makes it difficult to reconstruct high-precision brightness temperature map. The traditional regularization methods add a unique penalty all frequency bands solution, which may cause reconstructed result be too smooth retain certain features original map such as edge information. In this paper, multi-parameter method is proposed SAIR distribution. Different from classical single-parameter regularization, adds multiple different penalties can exhibit multi-scale characteristics Multiple parameters selected simplified multi-dimensional generalized cross-validation method. experimental results show that, compared with conventional total variation, Tikhonov, and band-limited methods, more detailed information better improve accuracy distribution, superior noise suppression, demonstrating effectiveness robustness