SARFormer -- An Acquisition Parameter Aware Vision Transformer for Synthetic Aperture Radar Data
This work addresses the challenge of handling complex SAR data geometry for improved performance in remote sensing applications, representing an incremental advancement in domain-specific methods.
The paper tackles the problem of processing synthetic aperture radar (SAR) images by introducing SARFormer, a Vision Transformer modified with an acquisition parameter encoding module, which achieves up to 17% improvement in RMSE over baselines on tasks like height reconstruction and segmentation.
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