γ-Net: Superresolving SAR Tomographic Inversion via Deep Learning

Abstract

Synthetic aperture radar tomography (TomoSAR) has been extensively employed in 3-D reconstruction in dense urban areas using high-resolution SAR acquisitions. Compressive sensing (CS)-based algorithms are generally considered as the state of the art in super-resolving TomoSAR, in particular in the single look case. This superior performance comes at the cost of extra computational burdens, because of the sparse reconstruction, which cannot be solved analytically and we need to employ computationally expensive iterative solvers. In this paper, we propose a novel deep learning-based super-resolving TomoSAR inversion approach, γ-Net, to tackle this challenge. γ-Net adopts advanced complex-valued learned iterative shrinkage thresholding algorithm (CV-LISTA) to mimic the iterative optimization step in sparse reconstruction. Simulations show the height estimate from a well-trained γ-Net approaches the Cramér-Rao lower bound while improving the computational efficiency by 1 to 2 orders of magnitude comparing to the first-order CS-based methods. It also shows no degradation in the super-resolution power comparing to the state-of-the-art second-order TomoSAR solvers, which are much more computationally expensive than the first-order methods. Specifically, γ-Net reaches more than 90% detection rate in moderate super-resolving cases at 25 measurements at 6dB SNR. Moreover, simulation at limited baselines demonstrates that the proposed algorithm outperforms the second-order CS-based method by a fair margin. Test on real TanDEM-X data with just 6 interferograms also shows high-quality 3-D reconstruction with high-density detected double scatterers.

Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-Resolving SAR Tomography

“…( 3) is extremely computational expensive. To overcome the heavy computational burden and make super-resolving TomoSAR inversion for large-scale processing feasible, the author proposed γ-Net in [23], which tailors the However, as one can see in Fig. 2, γ-Net inherits the 33 learning architecture of LISTA despite modifications made 34 by the authors to improve the performance.…”

“…In the simulation, we applied the same settings as [23], i.e. 25 regularly distributed spatial baselines in the range of -135m to 135m were simulated.…”

“…Results in [21] show that L-VAMP is able to separate overlaid scatterers. γ-Net was proposed in [23] by tailoring the complex-valued LISTA network. γ-Net introduced weight coupling structure [24] and support selection scheme [24] to each iteration block in LISTA and improved the conventional soft-thresholding function by piecewise linear function.…”

“…γ-Net introduced weight coupling structure [24] and support selection scheme [24] to each iteration block in LISTA and improved the conventional soft-thresholding function by piecewise linear function. It was demonstrated in [23] that γ-Net improves the computational efficiency by 2-3 order of magnitude comparing to the state-ofthe-art second-order TomoSAR solver SL1MMER [13] while showing no degradation in super-resolution ability and location accuracy.…”

TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

“…Compared with traditional sparse reconstruction algorithms, the deep unfolded network is usually more robust, converges faster and does not require parameter fine-tuning. Kun Qian et al developed a sparse unfolding network LISTA-CPSS [11] to solve the TomoSAR problem under the name of γ-net. However, its computational cost is still relatively high, and it relies on a very large volume of dataset in the training stage, which are both remaining obstacles in practice of TomoSAR.…”