Abstract
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.
Highlights
Synthetic aperture imaging radiometers (SAIRs) are passive microwave sensors by use of synthetic aperture technique
In order to overcome the above shortcomings of real aperture radiometers, SAIRs improve the spatial resolution by sparsely arranging small antennas
It has been demonstrated that the inverse problem of SAIRs is mathematically ill-posed so that the solution is neither unique nor stable [6]
Summary
Synthetic aperture imaging radiometers (SAIRs) are passive microwave sensors by use of synthetic aperture technique. An important application of SAIRs is to detect Sea Surface Salinity (SSS), which requires that the measurement accuracy of the ocean brightness temperature is within 0.1K. It should be noted that the three regularization methods mentioned above do not make use of any prior information about brightness temperature images of the observed scene in the spatial domain. It is easy to obtain some prior information such as the upper and lower bounds, especially for the observed scene like the open ocean with relatively small dynamic range of the brightness temperatures. THE IMAGING PRINCIPLE By measuring the complex correlation between the signals collected by two spatially separated antennas, which have an overlapping field of view, SAIRs yield samples of the complex visibilities of the brightness temperature of the observed scene. The relationship between the measured visibility function V(u) and the brightness temperature images TB(ξ) is given by [18]
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