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A 30 meter land cover mapping of China with an efficient clustering algorithm CBEST
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A 30 meter land cover mapping of China with an efficient clustering algorithm CBEST
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A 30 meter land cover mapping of China with an efficient clustering algorithm CBEST
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Journal Article
A 30 meter land cover mapping of China with an efficient clustering algorithm CBEST
2014
Overview
Remote sensing based land cover mapping at large scale is time consuming when using either supervised or unsupervised clas- sification approaches. This article used a fast clustering method---Clustering by Eigen Space Transformation （CBEST） to pro- duce a land cover map for China. Firstly, 508 Landsat TM scenes were collected and processed. Then, TM images were clus- tered by combining CBEST and K-means in each pre-defined ecological zone （50 in total for China）. Finally, the obtained clusters were visually interpreted as land cover types to complete a land cover map. Accuracy evaluation using 2159 test sam- pies indicates an overall accuracy of 71.7% and a Kappa coefficient of 0.64. Comparisons with two global land cover products （i.e., Finer Resolution Observation and Monitoring of Global Land Cover （FROM-GLC） and GlobCover 2009） also indicate that our land cover result using CBEST is superior in both land cover area estimation and visual effect for different land cover types.
