Sea surface temperature(SST) satellite images are often partially occluded by clouds. Image inpainting is one approach to restore the occluded region. Considering the sparseness of SST images, they can be restored via learning-based inpainting. However, state-of-the-art learning-based inpainting methods using deep neural networks require large amount of non-occluded images as a training set. Since most SST images contain occluded regions, it is hard to collect sufficient non-occluded images. In this paper, we propose a novel method that uses occluded images as training images hence we can enlarge the amount of available training images from a certain SST image set. This is realized by comprising a novel reconstruction loss and adversarial loss. Experimental results confirm the effectiveness of our method.