Inpainting of Remote Sensing SST Images With Deep Convolutional Generative Adversarial Network

Cloud occlusion is a common problem in the satellite remote sensing (RS) field and poses great challenges for image processing and object detection. Most existing methods for cloud occlusion recovery extract the surrounding information from the single corrupted image rather than the historical RS image records. Moreover, the existing algorithms can only handle small and regular-shaped obnubilation regions. This letter introduces a deep convolutional generative adversarial network to recover the RS sea surface temperature images with cloud occlusion from the big historical image records. We propose a new lass function for the inpainting network, which adds a supervision term to solve our specific problem. Given a trained generative model, we search for the closest encoding of the corrupted image in the low-dimensional space using our inpainting loss function. This encoding is then passed through the generative model to infer the missing content. We conduct experiments on the RS image data set from the national oceanic and atmospheric administration. Compared with traditional and machine learning methods, both qualitative and quantitative results show that our method has advantages over existing methods.