How Effective Is Super-Resolution to Improve Dense Labelling of Coarse Resolution Imagery?
Coarse resolution remote sensing images, such as LANDSAT and MODIS are easily found in public open repositories and, therefore, are widely used in many studies. But their use for automatic creation of thematic maps is very restrict since most of deep-based semantic segmentation (a.k.a dense labelling) approaches are only suitable for subdecimeter data. In this paper, we design a straightforward framework in order to evaluate the effectiveness of deep-based super-resolution in the semantic segmentation of low-resolution remote sensing images. We carried out an extensive set of experiments on three remote sensing datasets with distinct nature/properties. The results show that super-resolution is effective to improve semantic segmentation performance on low-resolution aerial imagery. It not only outperforms unsupervised interpolation but also achieves semantic segmentation results comparable to high-resolution data.
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