An autonomousendmember detection technique based on lattice associative memories and statistical clustering

We present an autonomous technique for the detection and extraction of all potential endmembers from hyperspectral imagery. The proposed technique is based on the convex polyhedral model. The computation of the vertices of a minimal polyhedron is accomplished using lattice auto-associave memories as well as other lattice algebra theoretic concepts. A novel statistical data clustering algorithm is used to select final endmembers.

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