Spectral data treatments for impervious endmember derivation and fraction mapping from Landsat ETM＋ imagery： a comparative analysis  

作　　者：Wei WANG Xinfeng YAO Minhe JI Jiao ZHANG 

机构地区：[1]Key Laboratory of GIScience (Ministry of Education of China), East China Normal University, Shanghai 200241, China [2]Agricultural Information Institute of Science and Technology, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China [3]China East-West Cooperation Research Center, East China Normal University, Shanghai 200241, China

出　　处：《Frontiers of Earth Science》2015年第2期179-191,共13页地球科学前沿（英文版）

摘　　要：Various spectral data preprocessing approaches have been used to improve endmember extraction for urban landscape decomposition, yet little is known of their comparative adequacy for impervious surface mapping. This study tested four commonly used spectral data treatment strategies for endmember derivation, including original spectra, image fusion via principal component analysis, spectral normalization, and the minimum noise fraction （MNF） transformation. Land cover endmembers derived using each strategy were used to build a linear spectral mixture analysis （LSMA） model in order to unmix treated image pixels into fraction maps, and an urban imperviousness map was generated by combining the fraction maps representing imperviousness endmembers. A cross-map comparative analysis was then performed to rank the four data treatment types based on such common evaluation indices as the coefficient of determination （R2） and root mean square error （RMSE）. A Landsat 7 ETM~ multispectral image covering the metropolitan region of Shanghai, China was used as the primary dataset, and the model results were evaluated using high-resolution color- infrared aerial photographs of roughly the same time period. The test results indicated that, with the highest R2 （0.812） and the lowest RMSE （0.097） among all four preprocessing treatments, the endmembers in the form of MNF-transformed spectra produced the best model output for characterizing urban impervious surfaces. The outcome of this study may provide useful guidance for future impervious surface mapping using medium-resolution remote sensing data.Various spectral data preprocessing approaches have been used to improve endmember extraction for urban landscape decomposition, yet little is known of their comparative adequacy for impervious surface mapping. This study tested four commonly used spectral data treatment strategies for endmember derivation, including original spectra, image fusion via principal component analysis, spectral normalization, and the minimum noise fraction （MNF） transformation. Land cover endmembers derived using each strategy were used to build a linear spectral mixture analysis （LSMA） model in order to unmix treated image pixels into fraction maps, and an urban imperviousness map was generated by combining the fraction maps representing imperviousness endmembers. A cross-map comparative analysis was then performed to rank the four data treatment types based on such common evaluation indices as the coefficient of determination （R2） and root mean square error （RMSE）. A Landsat 7 ETM~ multispectral image covering the metropolitan region of Shanghai, China was used as the primary dataset, and the model results were evaluated using high-resolution color- infrared aerial photographs of roughly the same time period. The test results indicated that, with the highest R2 （0.812） and the lowest RMSE （0.097） among all four preprocessing treatments, the endmembers in the form of MNF-transformed spectra produced the best model output for characterizing urban impervious surfaces. The outcome of this study may provide useful guidance for future impervious surface mapping using medium-resolution remote sensing data.

关 键 词：impervious surface estimation linear spectral 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] S127[自动化与计算机技术—计算机科学与技术]