SAR Strikes Back: A New Hope for RSVQA
It addresses the challenge of using SAR imagery for RSVQA, which is incremental as it builds on existing optical methods by introducing and comparing SAR data.
This work tackled the problem of integrating Synthetic Aperture Radar (SAR) data into Remote Sensing Visual Question Answering (RSVQA) to improve accuracy, showing that a two-stage model outperforms an end-to-end approach by nearly 10% and that decision-level fusion achieves an F1-micro score of 75.00% and overall accuracy of 75.49%.
Remote Sensing Visual Question Answering (RSVQA) is a task that extracts information from satellite images to answer questions in natural language, aiding image interpretation. While several methods exist for optical images with varying spectral bands and resolutions, only recently have high-resolution Synthetic Aperture Radar (SAR) images been explored. SAR's ability to operate in all weather conditions and capture electromagnetic features makes it a promising modality, yet no study has compared SAR and optical imagery in RSVQA or proposed effective fusion strategies. This work investigates how to integrate SAR data into RSVQA and how to best combine it with optical images. We present a dataset that enables SAR-based RSVQA and explore two pipelines for the task. The first is an end-to-end model, while the second is a two-stage framework: SAR information is first extracted and translated into text, which is then processed by a language model to produce the final answer. Our results show that the two-stage model performs better, improving accuracy by nearly 10% over the end-to-end approach. We also evaluate fusion strategies for combining SAR and optical data. A decision-level fusion yields the best results, with an F1-micro score of 75.00%, F1-average of 81.21%, and overall accuracy of 75.49% on the proposed dataset. SAR proves especially beneficial for questions related to specific land cover types, such as water areas, demonstrating its value as a complementary modality to optical imagery.