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Proceedings Paper

Finding endmember classes in hyperspectral imagery
Author(s): Cheng Gao; Yao Li; Chein-I Chang
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Paper Abstract

Endmember finding has received considerable interest in hyperspectral imaging. In reality an endmember finding algorithm (EFA) suffers from endmember variability which causes inaccuracy, inconsistency and instability. In this case a real endmember may not exist but rather appears as its variant, referred to as virtual signature (VS). This paper presents a new approach to finding VSs by taking endmember variability into account. It first determines a required number of endmember classes by virtual dimensionality (VD), then designs an unsupervised method to find endmember classes and finally develops an iterative algorithm to find VSs. Comprehensive experiments including synthetic and real image scenes are conducted to demonstrate effectiveness of the proposed approach.

Paper Details

Date Published: 21 May 2015
PDF: 11 pages
Proc. SPIE 9501, Satellite Data Compression, Communications, and Processing XI, 95010M (21 May 2015); doi: 10.1117/12.2176766
Show Author Affiliations
Cheng Gao, Univ. of Maryland, Baltimore County (United States)
Yao Li, Univ. of Maryland, Baltimore County (United States)
Chein-I Chang, Univ. of Maryland, Baltimore County (United States)


Published in SPIE Proceedings Vol. 9501:
Satellite Data Compression, Communications, and Processing XI
Bormin Huang; Chein-I Chang; Chulhee Lee; Yunsong Li; Qian Du, Editor(s)

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