An Improved Endmember Selection Method Based on Vector Length for MODIS Reflectance Channels
AbstractEndmember selection is the basis for sub-pixel land cover classifications using multiple endmember spectral mixture analysis (MESMA) that adopts variant endmember matrices for each pixel to mitigate errors caused by endmember variability in SMA. A spectral library covering a large number of endmembers can account for endmember variability, but it also lowers the computational efficiency. Therefore, an efficient endmember selection scheme to optimize the library is crucial to implement MESMA. In this study, we present an endmember selection method based on vector length. The spectra of a land cover class were divided into subsets using vector length intervals of the spectra, and the representative endmembers were derived from these subsets. Compared with the available endmember average RMSE (EAR) method, our approach improved the computational efficiency in endmember selection. The method accuracy was further evaluated using spectral libraries derived from the ground reference polygon and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery respectively. Results using the different spectral libraries indicated that MESMA combined with the new approach performed slightly better than EAR method, with Kappa coefficient improved from 0.75 to 0.78. A MODIS image was used to test the mapping fraction, and the representative spectra based on vector length successfully modeled more than 90% spectra of the MODIS pixels by 2-endmember models. View Full-Text
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Xu, Y.; Shi, J.; Du, J. An Improved Endmember Selection Method Based on Vector Length for MODIS Reflectance Channels. Remote Sens. 2015, 7, 6280-6295.
Xu Y, Shi J, Du J. An Improved Endmember Selection Method Based on Vector Length for MODIS Reflectance Channels. Remote Sensing. 2015; 7(5):6280-6295.Chicago/Turabian Style
Xu, Yuanliu; Shi, Jiancheng; Du, Jinyang. 2015. "An Improved Endmember Selection Method Based on Vector Length for MODIS Reflectance Channels." Remote Sens. 7, no. 5: 6280-6295.