Open AccessSuperpixel segmentation and machine learning classification algorithm for cloud detection in remote‐sensing images
Author(s) -
Shi Yueting,
Wang Weijiang,
Gong Qishu,
Li Dingyi
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0240
Subject(s) - softmax function , computer science , artificial intelligence , segmentation , cloud computing , cluster analysis , classifier (uml) , pattern recognition (psychology) , feature extraction , computer vision , remote sensing , deep learning , geography , operating system
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.