Paper
21 May 2015 Finding endmember classes in hyperspectral imagery
Author Affiliations +
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Cheng Gao, Yao Li, and Chein-I Chang "Finding endmember classes in hyperspectral imagery", Proc. SPIE 9501, Satellite Data Compression, Communications, and Processing XI, 95010M (21 May 2015); https://doi.org/10.1117/12.2176766
Lens.org Logo
CITATIONS
Cited by 2 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Algorithm development

Hyperspectral imaging

Signal to noise ratio

Minerals

Digital imaging

Interference (communication)

Data modeling

Back to Top