Publication
Title
Estimating the number of endmembers in hyperspectral imagery with nearest neighbor distances
Author
Abstract
We present a new method for estimating the number of end-members present in a hyperspectral data set, based on the scaling behavior of nearest-neighbor distances. We demonstrate the method on artificial data, and show that it has a low dependence on the spectral dimensionality or the size of the data set. Furthermore, the proposed technique gives consistent results over different random instances of the data, indicated by a low standard deviation. On the AVIRIS Cuprite and Indian Pines data set, this technique yields results that are comparable to those obtained via other methods.
Language
English
Source (book)
IEEE IGARSS 2012, International Geoscience and Remote Sensing Symposium , Munich, 22-27 July 2012
Publication
S.l. : 2012
ISBN
978-1-4673-1158-8
Volume/pages
p. 1377-1380
ISI
000313189401147
Full text (Publishers DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 28.11.2012
Last edited 23.03.2017
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