Mapping the Unseen: Exploiting Super-Resolution for Semantic Segmentation in Low-Resolution Images

  • Matheus B. Pereira UFMG
  • Jefersson Alex dos Santos UFMG


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.


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PEREIRA, Matheus B.; DOS SANTOS, Jefersson Alex. Mapping the Unseen: Exploiting Super-Resolution for Semantic Segmentation in Low-Resolution Images. In: WORKSHOP DE TESES E DISSERTAÇÕES - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 33. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 77-83. DOI: