Deep Matting for Cloud Detection in Remote Sensing Images

Cloud detection, as an important preprocessing operation for remote sensing (RS) image analysis, has received increasing attention in recent years. Most of the previous cloud detection methods consider the detection as a pixel-wise image classification problem (cloud versus background), which inevitably leads to a category-ambiguity when dealing with the detection of thin clouds. In this article, starting from the RS imaging mechanism on cloud images, we re-examine the cloud detection under a totally different point of view, i.e., to formulate cloud detection as a mixed energy separation between foreground and background images. This process can be further equivalently implemented under a deep learning-based image matting framework with a clear physical significance. More importantly, the proposed method is capable to deal with three different but related tasks, i.e., 'cloud detection,' 'cloud removal,' and 'cloud cover assessment,' under a unified framework. The experimental results on the three satellite image data sets demonstrate the effectiveness of our method, especially for those hard but common examples in RS images, such as the thin and wispy cloud.