A New Method of Cloud Detection Based on Cascaded AdaBoost

8th International Symposium of the Digital Earth

地球科学;计算机科学

Ma, C.^1 ; Chen, F.^1 ; Liu, J.^1 ; Duan, J.^1

Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, No.9 Deng Zhuang Road, Haidian District, Beijing, 100094, China^1

关键词: Ada boost classifiers;    AdaBoost algorithm;    Automatic cloud detection;    Cloud detection;    Different resolutions;    Effective algorithms;    Remote sensing images;    Spectral feature;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/18/1/012026/pdf
DOI  :  10.1088/1755-1315/18/1/012026

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Cloud detection of remote sensing image is a critical step in the processing of the remote sensing images. How to quickly, accurately and effectively detect cloud on remote sensing images, is still a challenging issue in this area. In order to avoid disadvantages of the current algorithms, the cascaded AdaBoost classifier algorithm is successfully applied to the cloud detection. A new algorithm combined cascaded AdaBoost classifier and multi-features, is proposed in this paper. First, multi-features based on the color, texture and spectral features are extracted from the remote sensing image. Second, the automatic cloud detection model is obtained based on the cascaded AdaBoost algorithm. In this paper, the results show that the new algorithm can determine cloud detection model and threshold values adaptively for different resolution remote sensing training data. The accuracy of cloud detection is improved. So it is a new effective algorithm for the cloud detection of remote sensing images.