Towards Super-Resolution SAR Tomography of Forest Areas using Deep Learning-Assisted Compressive Sensing

Abstract

<p>Forests are home to an estimated 80% of the world’s terrestrial biodiversity. They play a crucial role in mitigating climate change by absorbing and sequestering carbon dioxide. Given their importance, it is essential to monitor and understand their structure on a global scale. Synthetic Aperture Radar (SAR) Tomography (TomoSAR) allows to create a 3D image of the forest vertical profile. Compressive Sensing (CS)-based algorithms are considered as the state of the art in TomoSAR reconstruction, thanks to their super-resolution and extra robustness capabilities [1]. However, they also come with a great computational burden as the sparse reconstruction is achieved through iterative solvers. Recently, a deep neural network γ-net [2], was proposed, using the Learned Iterative Shrinkage Thresholding Algorithm (LISTA) as introduced in [3]. LISTA unrolls the original ISTA as a RNN structure and was further extended to the complex domain (CV-LISTA), to solve TomoSAR inversion. γ-net was evaluated on urban areas and showed no significant degradation in super-resolving power compared to state-of-the-art solvers, while reducing the computational burden by 2 or 3 orders of magnitude</p> <p>In this work, we adapt the γ-net approach to TomoSAR of forest areas. Since the backscattering profile of forest in SAR data shows a strong volumetric component making it non-sparse, it is unsuitable for direct CS reconstruction. Therefore, this work will study different transformations to represent the continuous backscattering profile of forest with a set of sparse coefficients after projecting to a certain orthogonal basis. We will experiment Wavelet transforms which have been employed in previous model-based CS TomoSAR reconstruction [4]. Experiments on learning the sparse transformation directly from data will also be conducted and compared to Wavelet transforms. The overall performance of the proposed algorithm will be evaluated based on its super-resolution power and its efficiency using simulated data. Experiments on real data in tropical and boreal forests will also be performed, in order to compare our solution with traditional baseline methods, such as Capon Beamforming, as well as state-of-the-art model-based methods.</p> <p>References<br /> [1] X. X. Zhu and R. Bamler, “Tomographic SAR Inversion by L1-Norm Regularization—The Compressive Sensing Approach”, IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 10, pp. 3839–3846, 2010.<br /> [2] K. Qian, Y. Wang, Y. Shi, and X. X. Zhu, “Y-Net: Superresolving SAR Tomographic Inversion via Deep Learning”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–16, 2022.<br /> [3] K. Gregor and Y. LeCun, “Learning Fast Approximations of Sparse Coding”, in Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML’10, (Madison, WI, USA), p. 399–406, Omnipress, 2010.<br /> [4] E. Aguilera, M. Nannini, and A. Reigber, “Wavelet-Based Compressed Sensing for SAR Tomography of Forested Areas”, IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 12, pp. 5283–5295, 2013.</p>