By using multi-source and multi-temporal remote sensing images

a ground object information recovery algorithm for thin cloud contaminated remote sensing images is proposed by combining classification with transfer learning.Firstly

multi-resolution decomposition of multi-source and multi-temporal remote sensing images is performed by using multi-directional nonsubsampled dual-tree complex wavelet transform.The decomposed high frequency coefficients of the ground objects of the thin cloud images are primarily classified by using Bayesian method.Then the transfer least square support vector regression model is trained to obtain the model parameters by using the domain adaptive learning of the low frequency coefficients of each class of ground objects.Finally

the low frequency coefficients of the thin cloud-contaminated images are predicted by using those of the cloudless reference images.The thin clouds are removed and the ground object information of the thin cloud contaminated images is recovered.Experimental results show that the ground objects recovered by the proposed algorithm have clear spatial details and small spectral distortion.Especially for the thin cloud contaminated remote sensing images with seasonal variation of ground objects

the proposed algorithm can effectively recover the ground object information contaminated by thin clouds.