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.

Abstract licence: CC BY-ND

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