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A New Growing Method for Simplex-Based Endmember Extraction Algorithm

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4 Author(s)
Chein-I Chang ; Dept. of Comput. Sci. & Electr. Eng., Maryland Univ., Baltimore, MD ; Chao-Cheng Wu ; Wei-min Liu ; Yen-Chieh Ouyang

A new growing method for simplex-based endmember extraction algorithms (EEAs), called simplex growing algorithm (SGA), is presented in this paper. It is a sequential algorithm to find a simplex with the maximum volume every time a new vertex is added. In order to terminate this algorithm a recently developed concept, virtual dimensionality (VD), is implemented as a stopping rule to determine the number of vertices required for the algorithm to generate. The SGA improves one commonly used EEA, the N-finder algorithm (N-FINDR) developed by Winter, by including a process of growing simplexes one vertex at a time until it reaches a desired number of vertices estimated by the VD, which results in a tremendous reduction of computational complexity. Additionally, it also judiciously selects an appropriate initial vector to avoid a dilemma caused by the use of random vectors as its initial condition in the N-FINDR where the N-FINDR generally produces different sets of final endmembers if different sets of randomly generated initial endmembers are used. In order to demonstrate the performance of the proposed SGA, the N-FINDR and two other EEAs, pixel purity index, and vertex component analysis are used for comparison

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Geoscience and Remote Sensing, IEEE Transactions on  (Volume:44 ,  Issue: 10 )