Cloud detection based on support vector machine with image features for GF-1 data
LI Xusheng1(), LIU Yufeng2(), CHEN Donghua2,3, LIU Saisai4, LI Hu2,3
1. College of Grass Industry and Environment, Xinjiang Agricultural University, Urumqi 830052, China 2. College of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China 3. College of Geography and Tourism, Anhui Normal University, Wuhu 241000, China 4. College of Geography and Tourism, Xinjiang Normal University, Urumqi 830001, China
In the GF-1 image data applications, applying cloud layers influences accuracy of information extraction and image utilization rate, to tackling this problem, this paper proposes a support vector machine cloud detection method combining image spectral features and texture features. For GF-1 data, the method of the gray-level co-occurrence matrix is used to extract those texture features. Spectral characteristics of clouds and ground and texture characteristics serve as feature vector, and support vector machine (SVM) is used to conduct cloud detection to GF-1 data. Studies have shown that the precision and recall of this method for all kinds of cloud detection are above 99.2% and 93.9%, and the error rate is below 1.1%, which is obviously better than the cloud detection algorithm using traditional support vector machine and maximum likelihood value, and it combines the image texture and spectral characteristics, and thus it has certain universality in theory.
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LI Xusheng, LIU Yufeng, CHEN Donghua, LIU Saisai, LI Hu. Cloud detection based on support vector machine with image features for GF-1 data. Remote Sensing for Land & Resources, 2020, 32(3): 55-62.
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