Mapping the Unseen: Exploiting Super-Resolution for Semantic Segmentation in Low-Resolution Images - Pereira, Matheus B.; dos Santos, Jefersson Alex (Anais Estendidos da Conference on Graphics, Patterns and Images (SIBGRAPI); 2020: Anais Estendidos da XXXIII Conference on Graphics, Patterns and ImagesDO - 10.5753/sibgrapi.est.2020.12987, 2020)
High-resolution aerial images are usually not accessible or affordable. On the other hand, low-resolution remote sensing data is easily found in public open repositories. The problem is that the low-resolution representation can compromise pattern recognition algorithms, especially semantic segmentation. In this M.Sc. dissertation1 , we design two frameworks in order to evaluate the effectiveness of super-resolution in the semantic segmentation of low-resolution remote sensing images. We carried out an extensive set of experiments on different remote sensing datasets. The results show that super-resolution is effective to improve semantic segmentation performance on low-resolution aerial imagery, outperforming unsupervised interpolation and achieving semantic segmentation results comparable to highresolution data.