Abstract:
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 ...Show MoreMetadata
Abstract:
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.
Published in: 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Date of Conference: 26-28 June 2013
Date Added to IEEE Xplore: 26 October 2017
ISBN Information:
Electronic ISSN: 2158-6276