Ground Object Information Recovery for Thin Cloud Contaminated Optical Remote Sensing Images

Аннотация

Ground object information on optical remote sensing images is obscured by thin clouds. This paper proposes a ground object information recovery algorithm for thin cloud contaminated optical remote sensing images by combining methods of support vector guidance filtering and transfer learning. Firstly, thin cloud contaminated target images and cloud-free images are decomposed into multidirectional subbands by using multi-directional nonsubsampled dual-tree complex wavelet packet transform (NS-DTCWPT). Then support vector guided filter is applied to remove thin cloud and transfer learning method is used to predict the ground object information on multidirectional subbands. Finally, the processed multidirectional subbands are reconstructed by using inverse multi-directional NS-DTCWPT to obtain the ground object information recovery images. The proposed algorithm combines the advantages of methods of support vector guidance filtering and transfer learning. Experimental results show that the proposed algorithm can effectively remove the thin clouds on the optical remote sensing images and obtain a good recovery effect of ground object information.