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A New Endmember Generation Algorithm Based on a Geometric Optimization Model for Hyperspectral Images

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4 Author(s)
Xiurui Geng ; Key Lab. of Technol. in Geo-spatial Inf. Process. & Applic. Syst., Inst. of Electron., Beijing, China ; Luyan Ji ; Yongchao Zhao ; Fuxiang Wang

This letter presents a new endmember generation method, which is called the geometric optimization model (GOM). The algorithm exploits the following fact: an L-dimensional (L-D) simplex can be divided into L + 1 L-D smaller simplexes by any point within the simplex, and the sum of the volumes of the L + 1 smaller simplexes is equal to the volume of the simplex. Based on this geometrical property, we propose a new objective function for endmember generation, whose variable only includes the mixing matrix. As a result, all the problems caused by the abundance matrix can be avoided. Experiments using both simulated and real hyperspectral data show that the GOM is effective in searching the optimal solution.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:10 ,  Issue: 4 )