Can SAR improve RSVQA performance?
This work addresses the problem of enhancing RSVQA for remote sensing applications by incorporating SAR data, though it is incremental as it builds on existing methods.
The study investigated whether Synthetic Aperture Radar (SAR) images can improve Remote Sensing Visual Question Answering (RSVQA) performance, finding that adding SAR modality leads to improved performances compared to using only optical images.
Remote sensing visual question answering (RSVQA) has been involved in several research in recent years, leading to an increase in new methods. RSVQA automatically extracts information from satellite images, so far only optical, and a question to automatically search for the answer in the image and provide it in a textual form. In our research, we study whether Synthetic Aperture Radar (SAR) images can be beneficial to this field. We divide our study into three phases which include classification methods and VQA. In the first one, we explore the classification results of SAR alone and investigate the best method to extract information from SAR data. Then, we study the combination of SAR and optical data. In the last phase, we investigate how SAR images and a combination of different modalities behave in RSVQA compared to a method only using optical images. We conclude that adding the SAR modality leads to improved performances, although further research on using SAR data to automatically answer questions is needed as well as more balanced datasets.