SARFormer -- An Acquisition Parameter Aware Vision Transformer for Synthetic Aperture Radar Data

This manuscript introduces SARFormer, a modified Vision Transformer (ViT) architecture designed for processing one or multiple synthetic aperture radar (SAR) images. Given the complex image geometry of SAR data, we propose an acquisition parameter encoding module that significantly guides the learning process, especially in the case of multiple images, leading to improved performance on downstream tasks. We further explore self-supervised pre-training, conduct experiments with limited labeled data, and benchmark our contribution and adaptations thoroughly in ablation experiments against a baseline, where the model is tested on tasks such as height reconstruction and segmentation. Our approach achieves up to 17% improvement in terms of RMSE over baseline models

Deep-Learning-Based Single-Image Height Reconstruction from Very-High-Resolution SAR Intensity Data

Originally developed in fields such as robotics and autonomous driving with image-based navigation in mind, deep learning-based single-image depth estimation (SIDE) has found great interest in the wider image analysis community. Remote sensing is no exception, as the possibility to estimate height maps from single aerial or satellite imagery bears great potential in the context of topographic reconstruction. A few pioneering investigations have demonstrated the general feasibility of single image height prediction from optical remote sensing images and motivate further studies in that direction. With this paper, we present the first-ever demonstration of deep learning-based single image height prediction for the other important sensor modality in remote sensing: synthetic aperture radar (SAR) data. Besides the adaptation of a convolutional neural network (CNN) architecture for SAR intensity images, we present a workflow for the generation of training data, and extensive experimental results for different SAR imaging modes and test sites. Since we put a particular emphasis on transferability, we are able to confirm that deep learning-based single-image height estimation is not only possible, but also transfers quite well to unseen data, even if acquired by different imaging modes and imaging parameters.