Superpixel segmentation and machine learning classification algorithm for cloud detection in remote-sensing images

Cloud detection is a fundamental yet challenging topic in remote-sensing image processing. The authors propose a method for multi-dimensional feature extraction and superpixel segmentation, and use a voting-based clustering ensemble to capture the whole target shape. In order to further identify clouds, snow-covered lands, and bright buildings on remote-sensing images, they first implement an Ostu threshold to get high grey-level sub-regions, and then extract the descriptors of these sub-regions and put them into the softmax regression classifier. Regarding these methods, the authors conduct experiments using GF-1 remote-sensing images. The results demonstrate the effectiveness and excellency of their proposed method.