Abstract:
For near-field synthetic aperture interferometric radiometer (SAIR) systems, imaging distance plays an important role in the imaging reconstruction. However, most current...Show MoreMetadata
Abstract:
For near-field synthetic aperture interferometric radiometer (SAIR) systems, imaging distance plays an important role in the imaging reconstruction. However, most current SAIR imaging algorithms treat the imaging distance as an exact known value, which may have a serious influence on imaging reconstruction quality caused by the misestimate of the practical imaging distance. In this letter, we propose a distance adaptive near-field SAIR imaging algorithm based on deep learning (DL) approach to handle this problem. The complex correlation operation block (CCOB) is proposed to predict the distance parameter and reconstruct the image first. And the residual attention enhanced module is utilized to further improve the imaging quality. The simulation results indicate that the proposed algorithm can accurately predict the imaging distance and achieve high-precision image reconstruction at different distances.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 20)