Title
Estimating the number of endmembers in hyperspectral imagery with nearest neighbor distancesEstimating the number of endmembers in hyperspectral imagery with nearest neighbor distances
Author
Faculty/Department
Faculty of Sciences. Physics
Research group
Vision lab
Publication type
bookPart
Publication
S.l. , [*]
Subject
Economics
Physics
Source (book)
IEEE IGARSS 2012, International Geoscience and Remote Sensing Symposium , Munich, 22-27 July 2012
ISSN
2153-6996
ISBN - Hoofdstuk
978-1-4673-1158-8
ISI
000313189401147
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
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.
E-info
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