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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.

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