MRF Based Spatial Complexity for Hyperspectral Imagery Unmixing View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2006

AUTHORS

Sen Jia , Yuntao Qian

ABSTRACT

Hyperspectral imagery (HSI) unmixing is a process that decomposes pixel spectra into a collection of constituent spectra (endmembers) and their correspondent abundance fractions. Without knowing any knowledge of HSI data, the unmixing problem is transformed into a blind source separation (BSS) problem. Several methods have been proposed to deal with the problem, like independent component analysis (ICA). In this paper, we introduce spatial complexity that applies Markov random field (MRF) to characterize the spatial correlation information of abundance fractions. Compared to previous BSS techniques for HSI unmixing, the major advantage of our approach is that it totally considers HSI spatial structure. Additionally, a proof is given that spatial complexity is suitable for HSI unmixing. Encouraging results have been obtained in terms of unmixing accuracy, suggesting the effectiveness of our approach. More... »

PAGES

531-540

Book

TITLE

Structural, Syntactic, and Statistical Pattern Recognition

ISBN

978-3-540-37236-3
978-3-540-37241-7

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/11815921_58

DOI

http://dx.doi.org/10.1007/11815921_58

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1028487450


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