Image Reconstruction of Synthetic Aperture Radiometer by Transformer

Abstract

In passive microwave remote sensing of the earth, compared with real aperture radiometer, synthetic aperture radiometer is a very powerful instrument with many advantages. However, the system design is more complex than the real aperture radiometer, and the hardware ideality is often not guaranteed. The non-ideal characteristics of the system hardware will bring a variety of errors to the system, which will cause the Fourier transform relationship between the visibility function and the brightness temperature image to no longer be established, thus reducing the quality of microwave brightness temperature image reconstruction by traditional methods. In this paper, a new microwave brightness temperature image reconstruction method for synthetic aperture radiometer by Transformer is proposed. This method uses a specially designed Transformer structure to extract the spectrum features in the visibility function. This method learns the mapping relationship between the visibility function and the original scene brightness temperature image through the supervised learning method, and learns as much as possible the spectrum information contained in the original scene brightness temperature image. Moreover, when there are missing baselines, this method will supplement the missing observation frequency information, so as to obtain better reconstructed image quality. With the above advantages, this method can suppress the Gibbs oscillation, and greatly reduce the side lobe. Compared with the existing reconstruction methods, whether missing baselines or not, the proposed image reconstruction method by Transformer has advantages in image quality. We verify the performance of this brightness temperature image reconstruction method through simulation and experiment.