The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W12-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 61–66, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-61-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 61–66, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-61-2020

  04 Nov 2020

04 Nov 2020

AN END-TO-END FRAMEWORK FOR LOW-RESOLUTION REMOTE SENSING SEMANTIC SEGMENTATION

M. B. Pereira and J. A. dos Santos M. B. Pereira and J. A. dos Santos
  • Dept. of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

Keywords: super-resolution, semantic segmentation, remote sensing, end-to-end framework

Abstract. High-resolution images for remote sensing applications are often not affordable or accessible, especially when in need of a wide temporal span of recordings. Given the easy access to low-resolution (LR) images from satellites, many remote sensing works rely on this type of data. The problem is that LR images are not appropriate for semantic segmentation, due to the need for high-quality data for accurate pixel prediction for this task. In this paper, we propose an end-to-end framework that unites a super-resolution and a semantic segmentation module in order to produce accurate thematic maps from LR inputs. It allows the semantic segmentation network to conduct the reconstruction process, modifying the input image with helpful textures. We evaluate the framework with three remote sensing datasets. The results show that the framework is capable of achieving a semantic segmentation performance close to native high-resolution data, while also surpassing the performance of a network trained with LR inputs.