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Proceedings Paper

An endmember extraction strategy for geometrical methods based on spectral-spatial information
Author(s): M. Beauchemin
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Paper Abstract

A two-step strategy for endmember extraction is presented. The goal of the first step is to create two pools of spectra, one containing potential endmember candidates and the other one representing spectra that are unquestionably convex combinations (mixed spectra). The second step consists in the application of a sub-optimal subset search method that is applied for best endmember combination. In the first step, vector order statistics are used to identify a medoid spectrum within non-overlapping spatial windows. Endmember extraction based on the iterative error analysis algorithm is then performed on the medoid subset to identify a set of medoid endmembers. The latter are subsequently used to spectrally unmix the original dataset. Spectra that are outside the hyper-surface (outliers) derived from the medoid endmembers represent the pool of potential endmembers. Medoid spectra residing inside the hyper-surface (inliers) constitute the mixed spectra pool. The inliers/outliers status of each spectrum of the original dataset is derived from conditions on their computed unmixing fraction values. Clustering analysis is next performed on the endmember pool of candidates to produce a set of exemplars. Spectral screening is applied on the inliers set to eliminate redundancy. In the second step, the oscillating feature subset search algorithm is applied to identify the endmember combination that best reconstruct, in the least squares sense, the spectra in the joint pools. Results of the proposed strategy are presented for synthetic and real hyperspectral data.

Paper Details

Date Published: 26 October 2011
PDF: 7 pages
Proc. SPIE 8180, Image and Signal Processing for Remote Sensing XVII, 81800N (26 October 2011); doi: 10.1117/12.897727
Show Author Affiliations
M. Beauchemin, Natural Resources Canada (Canada)


Published in SPIE Proceedings Vol. 8180:
Image and Signal Processing for Remote Sensing XVII
Lorenzo Bruzzone, Editor(s)

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