A Cloud Detection Algorithm for MODIS Images Combining Kmeans Clustering and Multi-Spectral Threshold Method
WANG Wei1, SONG Wei-guo1*, LIU Shi-xing2, ZHANG Yong-ming1, ZHENG Hong-yang1, TIAN Wei1
1. State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230027, China 2. Hefei University of Technology, Hefei 230009, China
Abstract：An improved method for detecting cloud combining Kmeans clustering and the multi-spectral threshold approach is described. On the basis of landmark spectrum analysis, MODIS data is categorized into two major types initially by Kmeans method. The first class includes clouds, smoke and snow, and the second class includes vegetation, water and land. Then a multi-spectral threshold detection is applied to eliminate interference such as smoke and snow for the first class. The method is tested with MODIS data at different time under different underlying surface conditions. By visual method to test the performance of the algorithm, it was found that the algorithm can effectively detect smaller area of cloud pixels and exclude the interference of underlying surface, which provides a good foundation for the next fire detection approach.
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