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Frontiers of Earth Science

Front. Earth Sci.    2015, Vol. 9 Issue (2) : 179-191     DOI: 10.1007/s11707-014-0456-5
RESEARCH ARTICLE |
Spectral data treatments for impervious endmember derivation and fraction mapping from Landsat ETM+ imagery: a comparative analysis
Wei WANG1,Xinfeng YAO2,Minhe JI1,3,*(),Jiao ZHANG1
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
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Abstract

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.

Keywords impervious surface estimation      linear spectral mixture analysis      minimum noise fraction      spectral normalization      image fusion     
Corresponding Authors: Minhe JI   
Online First Date: 31 July 2014    Issue Date: 30 April 2015
 Cite this article:   
Wei WANG,Xinfeng YAO,Minhe JI, et al. Spectral data treatments for impervious endmember derivation and fraction mapping from Landsat ETM+ imagery: a comparative analysis[J]. Front. Earth Sci., 2015, 9(2): 179-191.
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http://journal.hep.com.cn/fesci/EN/10.1007/s11707-014-0456-5
http://journal.hep.com.cn/fesci/EN/Y2015/V9/I2/179
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Wei WANG
Xinfeng YAO
Minhe JI
Jiao ZHANG
Fig.1  Study area: Shanghai CBD and its vicinity (The red box delineates the main study area, and the blue box indicates the coverage of validation data).
Fig.2  Procedure for comparing the effects of different spectral data treatment techniques on impervious surface mapping.
Fig.3  Reflectance (original or transformed) characteristics of endmembers for the four data treatment types: (a) original spectra, (b) fused spectra, (c) normalized spectra, and (d) MNF spectra.
Fig.4  Fraction maps resulting from the four data treatment types (columns from left to right: high albedo imperviousness, low albedo imperviousness, vegetation, and soil.
Fig.5  Urban imperviousness maps resulting from the four data treatment types.
Fig.6  Cross-treatment comparison of imperviousness fraction maps.
Error Original LSMA Fused LSMA Normalized LSMA MNF LSMA
R2 0.7952 0.5185 0.7642 0.8120
RMSE 0.0972 0.1465 0.1417 0.0965
Tab.1  Accuracy measures of impervious surface fraction maps induced from different spectra: original, fused, normalized, and MNF
Fig.7  Scatterplots and regression lines of accuracy validation sample points for each type of data treatment: (a) original, (b) fused, (c) normalized, and (d) MNF.
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