Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1079-1083, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1079-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1079-1083, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1079-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

THIN CLOUD DETECTION METHOD BY LINEAR COMBINATION MODEL OF CLOUD IMAGE

L. Liu, J. Li, Y. Wang, Y. Xiao, W. Zhang, and S. Zhang L. Liu et al.
  • Institute of Land and Resources Engineering, Kunming University of Science and Technology, Kunming, China

Keywords: Cloud Detection, Image Features, Grayscale, Texture, Linear Model, AdaBoost Classifier

Abstract. The existing cloud detection methods in photogrammetry often extract the image features from remote sensing images directly, and then use them to classify images into cloud or other things. But when the cloud is thin and small, these methods will be inaccurate. In this paper, a linear combination model of cloud images is proposed, by using this model, the underlying surface information of remote sensing images can be removed. So the cloud detection result can become more accurate. Firstly, the automatic cloud detection program in this paper uses the linear combination model to split the cloud information and surface information in the transparent cloud images, then uses different image features to recognize the cloud parts. In consideration of the computational efficiency, AdaBoost Classifier was introduced to combine the different features to establish a cloud classifier. AdaBoost Classifier can select the most effective features from many normal features, so the calculation time is largely reduced. Finally, we selected a cloud detection method based on tree structure and a multiple feature detection method using SVM classifier to compare with the proposed method, the experimental data shows that the proposed cloud detection program in this paper has high accuracy and fast calculation speed.