Estimation of impervious surface based on semi-constrained spectral mixture analysis
ZHU Honglei1,2, LI Ying1, LIU Zhaoli1, FU Bolin1,2
1. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China
Impervious surface plays an important role in monitoring urban sprawl and understanding human activities. Linear spectral mixture analysis (LSMA) is commonly used to estimate impervious surface due to its simple structure and clear physical meaning. However, previous researches found that LSMA seemed to overestimate slightly impervious surface fraction in less developed areas (0-20%) but underestimate it in the central business district (CBD) (over 80%). To tackle this problem, the authors developed impervious surface of Fujin Town in Heilongjiang Province from the Landsat Thematic Mapper (TM) image by using LSMA model under different constrained conditions and end-members. The results indicated that three end-members (high albedo, soil, and vegetation) semi-constrained LSMA provided a fine performance with a RMSE of 16.71%. Moreover, the paddy field in impervious surface fraction image was removed by using land surface temperature and vegetation coverage data.
朱红雷, 李颖, 刘兆礼, 付波霖. 基于半约束条件下不透水面的遥感提取方法[J]. 国土资源遥感, 2014, 26(2): 48-53.
ZHU Honglei, LI Ying, LIU Zhaoli, FU Bolin. Estimation of impervious surface based on semi-constrained spectral mixture analysis. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(2): 48-53.
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2014, Vol. 26 
