Special Section on Airborne Hyperspectral Remote Sensing of Urban Environments

Modified multiple endmember spectral mixture analysis for mapping impervious surfaces in urban environments

[+] Author Affiliations
Kun Tan

China University of Mining and Technology, Jiangsu Key Laboratory of Resources and Environment Information Engineering, Xuzhou, Jiangsu 221116, China

Xiao Jin

China University of Mining and Technology, Jiangsu Key Laboratory of Resources and Environment Information Engineering, Xuzhou, Jiangsu 221116, China

Qian Du

Mississippi State University, Department of Electrical and Computer Engineering, Starkville, Mississippi 39762, United States

Peijun Du

Nanjing University, Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing, Jiangsu 210046, China

J. Appl. Remote Sens. 8(1), 085096 (Aug 08, 2014). doi:10.1117/1.JRS.8.085096
History: Received May 20, 2014; Revised June 30, 2014; Accepted July 15, 2014
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Abstract.  A modified multiple endmember spectral mixture analysis (MMESMA) approach is proposed for high-spatial-resolution hyperspectral imagery in the application of impervious surface mapping. Different from the original MESMA that usually selects one endmember spectral signature for each land-cover class, the proposed MMESMA allows the selection of multiple endmember signatures for each land-cover class. It is expected that the MMESMA can better accommodate within-class variations and yield better mapping results. Various unmixing models are compared, such as the linear mixing model, linear spectral mixture analysis using the original linear mixture model, original MESMA, and support vector machine using a nonlinear mixture model. Airborne 1-m resolution HySpex and ROSIS data are used in the experiments. For HySpex data, validation based on 25-cm synchronism aerial photography shows that MMESMA performs the best, with the root-mean-squared error (RMSE) of the estimated abundance fractions being 13.20% and the correlation coefficient (R2) being 0.9656. For ROSIS data, validation based on simulation shows that MMESMA performs the best, with the RMSE of the estimated abundance fraction being 4.51% and R2 being 0.9878. These demonstrate that the proposed MMESMA can generate more reliable abundance fractions for high-spatial-resolution hyperspectral imagery, which tends to include strong within-class spectral variations.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Kun Tan ; Xiao Jin ; Qian Du and Peijun Du
"Modified multiple endmember spectral mixture analysis for mapping impervious surfaces in urban environments", J. Appl. Remote Sens. 8(1), 085096 (Aug 08, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.085096


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