Synthetic aperture imaging radiometers (SAIRs) are powerful passive microwave systems for high-resolution imaging by use of synthetic aperture technique. However, the ill-posed inverse problem for SAIRs makes it difficult to reconstruct the high-precision brightness temperature map. The traditional regularization methods add a unique penalty to all the frequency bands of the solution, which may cause the reconstructed result to be too smooth to retain certain features of the original brightness temperature map such as the edge information. In this paper, a multi-parameter regularization method is proposed to reconstruct SAIR brightness temperature distribution. Different from classical single-parameter regularization, the multi-parameter regularization adds multiple different penalties which can exhibit multi-scale characteristics of the original distribution. Multiple regularization parameters are selected by use of the simplified multi-dimensional generalized cross-validation method. The experimental results show that, compared with the conventional total variation, Tikhonov, and band-limited regularization methods, the multi-parameter regularization method can retain more detailed information and better improve the accuracy of the reconstructed brightness temperature distribution, and exhibit superior noise suppression, demonstrating the effectiveness and the robustness of the proposed method.
Synthetic aperture imaging radiometers (SAIRs) are powerful instruments for high-resolution Earth observation by use of small-aperture antennas sparsely arranged to achieve a large-aperture antenna. High-precision reconstruction algorithm is one of the key contents of SAIRs. Owing to the ill-posed problem and band-limited physical characteristic, there is a still large residual error for traditional regularization methods. It should be noted that the prior information like the lower and upper bounds of the brightness temperature distributions has not been utilized in the reconstruction procedure, especially for the open ocean with relatively small brightness temperature difference. In order to reduce the reconstruction error in SAIRs, a reconstruction method based on active set algorithm is presented by solving the least squares problems with lower and upper bounds. The simulation experiment results show that the proposed method can more effectively reduce the reconstruction error and better improve the accuracy of retrieved brightness temperature distributions in SAIRs than the band-limited regularization method, demonstrating the effectiveness of the proposed method.INDEX TERMS Imaging radiometry, synthetic aperture, inverse problem, reconstruction error.
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