Title
A distance geometric framework for non-linear hyperspectral unmixing A distance geometric framework for non-linear hyperspectral unmixing
Author
Faculty/Department
Faculty of Sciences. Physics
Publication type
article
Publication
New York (N.Y.) :IEEE ,
Subject
Economics
Physics
Engineering sciences. Technology
Source (journal)
IEEE journal of selected topics in applied earth observation and remote sensing / IEEE geoscience and remote sensing society; IEEE committee on earth observations. - New York (N.Y.)
Volume/pages
7(2014) :6 , p. 1879-1888
ISSN
1939-1404
ISI
000340621200004
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
In this article, a distance geometry-based framework for hyperspectral image unmixing is presented. A manifold representation of the data set is generated by creation of a nearest-neighbor graph on which shortest paths are calculated yielding a geodesic distance matrix. Instead of unfolding the manifold in a lower-dimensional Euclidean space, it is proposed to work directly on the manifold. To do so, algorithms need to be rewritten in terms of distance geometry. Building further on earlier work, where distance-based dimensionality estimation and endmember extraction methods were presented, we will propose a distance geometric version of the actual unmixing (abundance estimation) step. In this way, a complete distance geometric unmixing framework is obtained that is efficient compared to the classical methods based on optimization. Furthermore, the distance geometry-adapted algorithms can be applied on nonlinear data manifolds by employing geodesic distances. In the experiments, we demonstrate this by comparing the obtained nonlinear framework to its linear counterpart.
E-info
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000340621200004&DestLinkType=RelatedRecords&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000340621200004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000340621200004&DestLinkType=CitingArticles&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
Handle