SLIC-Based Cloud Removal Approach with Inpainting for Landsat 8 SAR Images

Abstract

Clouds which exist in optical remote sensing images can degrade their applicability for earth observation. Ground-cover information is degraded by thin clouds or even completely occluded by thick clouds, which limits further analysis and applications. Thus a SLIC based cloud removal approach is formulated for clustering the similar superpixels and forming Column Stack. Group Sparsity Constrained Robust principal component analysis is used to detect cloud and generate a column stack mask. Discriminative Robust Principal Component analysis is conducted to remove clouds. Finally inpainting is performed by finding the similar patches to obtain the gaps filled. The experimental results of reconstructed Landsat 8 real images are compared with the Landsat 7 real images using PSNR and RMSE. The values for PSNR are varying from 25 in Landsat 7 Real images to 90 in Landsat 8 reconstructed real images in Green channel and RMSE has changed from 175 in Landsat 7 real images to 3 in Red channel. This indicates that Landsat 8 reconstructed real images have greater quality and error rate in less.