Can SAR improve RSVQA performance?
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910580859731968 |
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| author | Tosato, Lucrezia Lobry, Sylvain Weissgerber, Flora Wendling, Laurent |
| author_facet | Tosato, Lucrezia Lobry, Sylvain Weissgerber, Flora Wendling, Laurent |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15642 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can SAR improve RSVQA performance? Tosato, Lucrezia Lobry, Sylvain Weissgerber, Flora Wendling, Laurent Computer Vision and Pattern Recognition 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. |
| title | Can SAR improve RSVQA performance? |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.15642 |