Performance of DUPA-RPCA

Performance of DUPA-RPCA

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p>In this paper, we consider the problem of removing clouds and recovering ground cover information from remote sensing images by proposing novel framework based on a deep unfolded and prior-aided robust principal component analysis (DUPA-RPCA) network. Clouds, together with their shadows, usually occlude ground-cover features in optical remote sen...

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... this section, we compare DUPA-RPCA with the spatial method of Single Image [5] and the multi-temporal RPCA based method of Coarse-to-fine [7]. Fig. 4 shows the performance of these algorithms based on peak signal-to-noise ratio (PSNR) together with the time taken by these algorithms. These results are obtained on 15 image sequences consisting of both Landsat-8 and Sentinel-2 images and prepared in a similar manner as the additional 70 images on which we trained our network. The ...

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Preprint
Full-text available
p>In this paper, we consider the problem of removing clouds and recovering ground cover information from remote sensing images by proposing novel framework based on a deep unfolded and prior-aided robust principal component analysis (DUPA-RPCA) network. Clouds, together with their shadows, usually occlude ground-cover features in optical remote sen...