Abstract
Growing Simplex Volume Analysis (GSVA) has recently been developed as an alternative theory to the Simplex Volume Analysis (SVA) theory discussed in Chap. 6 and shown to be a promising approach to finding endmembers. As a matter of fact, the Simplex Growing Algorithm (SGA) developed by Chang et al. (2006) for GSVA does the same as the N-finder algorithm (N-FINDR) developed by Winter (1999a, b) for SVA. The key difference between these two is how endmembers are found by the algorithms. For SVA the number of all endmembers must be known beforehand. SVA then finds all the endmembers together through simultaneous replacement of these endmembers. By contrast, GSVA does not need to know the number of endmembers a priori. Instead, it grows simplexes successively to find endmembers one at a time. Such a growing process is terminated by a specific stopping rule designed for a particular application. Accordingly, SVA can be considered as a sequential process compared to GSVA, which is a progressive process. Nevertheless, both theories are indeed closely related one way or the other. This chapter studies GSVA and develops various algorithms to explore their relationships to SVA from a progressive perspective.
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Chang, CI. (2016). Fully Geometric-Constrained Progressive Endmember Finding: Growing Simplex Volume Analysis. In: Real-Time Progressive Hyperspectral Image Processing. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6187-7_10
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