Journal
SCIENCE CHINA-EARTH SCIENCES
Volume 57, Issue 10, Pages 2293-2304Publisher
SCIENCE PRESS
DOI: 10.1007/s11430-014-4917-1
Keywords
land cover; mapping; cluster; Landsat TM; CBEST
Categories
Funding
- National High-tech R&D Program of China [2009AA12200101]
- Tsinghua University [2012Z02287]
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Remote sensing based land cover mapping at large scale is time consuming when using either supervised or unsupervised classification approaches. This article used a fast clustering method-Clustering by Eigen Space Transformation (CBEST) to produce a land cover map for China. Firstly, 508 Landsat TM scenes were collected and processed. Then, TM images were clustered 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 samples 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.
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