Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (3): 622-633.doi: 10.1007/s11390-019-1931-y

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

• Artificial Intelligence and Pattern Recognition • Previous Articles     Next Articles

Cloud Detection Using Super Pixel Classification and Semantic Segmentation

Han Liu1,2, Hang Du1,2, Dan Zeng1,2,*, Qi Tian3, Fellow, IEEE   

  1. 1 Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China;
    2 Shanghai Institute of Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China;
    3 Department of Computer Science, The University of Texas at San Antonio, San Antonio, U.S.A.
  • Received:2018-10-19 Revised:2019-03-27 Online:2019-05-05 Published:2019-05-06
  • Contact: Dan Zeng
  • About author:Han Liu received his B.E. degree in communication and information engineering from Shanghai University, Shanghai, in 2017. He is currently pursuing his M.E. degree at Key Laboratory of Specialty Fiber Optics and Optical Access Networks and Shanghai Institute of Advanced Communication and Data Science, Shanghai University, Shanghai. His research interests focus on computer vision and pattern recognition.
  • Supported by:
    This work was supported in part by ARO under Grant No. W911NF-15-1-0290 and Faculty Research Gift Awards by NEC Laboratories of America and Blippar to Dr. Qi Tian. It was also supported in part by the National Natural Science Foundation of China under Grant Nos. 61429201 and 61572307.

Cloud detection plays a very significant role in remote sensing image processing. This paper introduces a cloud detection method based on super pixel level classification and semantic segmentation. Firstly, remote sensing images are segmented into super pixels. Segmented super pixels compose a super pixel level remote sensing image database. Though cloud detection is essentially a binary classification task, our database is labeled into four categories to improve the generalization ability:thick cloud, cirrus cloud, building, and other culture. Secondly, the super pixel level database is used to train our cloud detection models based on convolution neural network (CNN) and deep forest. Hierarchical fusion CNN is proposed considering super pixel level images contain less semantic information than normal images. Taking full advantage of low-level features like color and texture information, it is more applicable for super pixel level classification. Besides, a distance metric is proposed to refine ambiguous super pixels. Thirdly, an end-to-end cloud detection model based on semantic segmentation is introduced. This model has no restrictions on the input size, and takes less time. Experimental results show that compared with other cloud detection methods, our proposed method achieves better performance.

Key words: cloud detection; convolution neural network; deep forest; semantic segmentation;

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