Finding endmember classes in hyperspectral imagery

Cheng Gao; Yao Li; Chein-I Chang

doi:10.1117/12.2176766

21 May 2015 Finding endmember classes in hyperspectral imagery

Cheng Gao; Yao Li; Chein-I Chang

Author Affiliations +

Proceedings Volume 9501, Satellite Data Compression, Communications, and Processing XI; 95010M (2015) https://doi.org/10.1117/12.2176766
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, Maryland, United States

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.

Citation Download Citation

Cheng Gao, Cheng Gao, Yao Li, Yao Li, Chein-I Chang, Chein-I Chang, } "Finding endmember classes in hyperspectral imagery", Proc. SPIE 9501, Satellite Data Compression, Communications, and Processing XI, 95010M (21 May 2015); doi: 10.1117/12.2176766; https://doi.org/10.1117/12.2176766

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