A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging

TL;DR

This paper introduces a novel deep unfolding network that leverages multi-dimensional features for tomographic SAR imaging, improving image completeness and accuracy over traditional methods.

Contribution

It proposes a two-dimensional deep unfolding network with added convolutional modules to better utilize inter-unit correlations in tomoSAR imaging.

Findings

Outperforms CS-based FISTA in image completeness and accuracy.

Surpasses DL-based gamma-Net in imaging performance.

Effective in reconstructing buildings from simulated data.

Abstract

Deep learning (DL)-based tomographic SAR imaging algorithms are gradually being studied. Typically, they use an unfolding network to mimic the iterative calculation of the classical compressive sensing (CS)-based methods and process each range-azimuth unit individually. However, only one-dimensional features are effectively utilized in this way. The correlation between adjacent resolution units is ignored directly. To address that, we propose a new model-data-driven network to achieve tomoSAR imaging based on multi-dimensional features. Guided by the deep unfolding methodology, a two-dimensional deep unfolding imaging network is constructed. On the basis of it, we add two 2D processing modules, both convolutional encoder-decoder structures, to enhance multi-dimensional features of the imaging scene effectively. Meanwhile, to train the proposed multifeature-based imaging network, we construct a tomoSAR simulation dataset consisting entirely of simulation data of buildings. Experiments verify the effectiveness of the model. Compared with the conventional CS-based FISTA method and DL-based gamma-Net method, the result of our proposed method has better performance on completeness while having decent imaging accuracy.