An Iterative Multidimensional Feature Reconstruction Network for Tomographic SAR Imaging

The tomographic Synthetic Aperture Radar (SAR) reconstruction based on deep learning (DL) can achieve high precision and efficiency, making it a promising method for various applications. However, current deep learning-based methods perform reconstruction by separately processing each range-azimuth resolution unit, which limits the utilization of features between resolution units and ignores the three-dimensional characteristics of the target. To address this limitation, we propose an iterative multidimensional feature reconstruction network that improves the accuracy of reconstruction by introducing correlation constraints between resolution units. Specifically, the proposed method slices the pulse-compressed data along the azimuth direction and treats each range-elevation slice as a calculation unit for reconstruction processing. And a multidimensional feature regularization module is designed to iteratively process two-dimensional features within the slice, taking into account the correlation between resolution units. Real-data experiments demonstrate that our proposed method outperforms existing network that utilize L
1
-norm sparse regularization in terms of achieving higher completeness.