SSMSS: A Model of Semantic Segmentation for Matching of Satellite and Sonar Images - Quispe, Marco Antonio Quiroz; Ramos, Jose David Garcia; Brião, Stephanie Loi; Díaz-Amado, José Alberto; Drews-Jr, Paulo Lilles Jorge (Anais do Simpósio Brasileiro de Robótica e Simpósio Latino Americano de Robótica (SBR/LARS); 2025: Anais do XVII Simpósio Brasileiro de Robótica e XVI Workshop de Robótica na Educação, 2025)
Establishing accurate correspondence between sonar and satellite images is a nontrivial task due to differences in modality, resolution, and environmental noise, especially in underwater scenarios with GPS-denied environments. This work investigates the integration of semantic segmentation for matching satellite and sonar images. We evaluated a diverse set of state-of-the-art architectures, including convolutional models (U-Net, U-Net++, FPN, PSPNet, LinkNet, and DeepLab v3+) and attention-based models (MA-Net and SegFormer), with a focus on their ability to capture local structures, multiscale features, and contextual dependencies relevant for robust cross-modal matching. Experimental results indicate that, while convolutional networks deliver efficient and accurate segmentation of salient structures, attention-based models improve matching performance in complex scenarios by modeling long-range spatial dependencies. Among the architectures evaluated, MA-Net achieves superior performance, with a pixel accuracy of 0.9519, a mean IoU of 0.9494, and a matching score of 0.3618, underscoring the effectiveness of attention mechanisms. These findings lay the groundwork for future research on unified segmentation and matching frameworks specifically designed for autonomous underwater navigation.