Title
Hyperspectral intrinsic dimensionality estimation with nearest-neighbor distance ratios Hyperspectral intrinsic dimensionality estimation with nearest-neighbor distance ratios
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
6(2013) :22 , p. 570-579
ISSN
1939-1404
ISI
000319278000011
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
The first task to be performed in most hyperspectral unmixing chains is the estimation of the number of endmembers. Several techniques for this problem have already been proposed, but the class of fractal techniques for intrinsic dimensionality estimation is often overlooked. In this paper, we study an intrinsic dimensionality estimation technique based on the known scaling behavior of nearest-neighbor distance ratios, and its performance on the spectral unmixing problem. We present the relation between intrinsic manifold dimensionality and the number of endmembers in a mixing model, and investigate the effects of denoising and the statistics on the algorithm. The algorithm is compared with several alternative methods, such as Hysime, virtual dimensionality, and several fractal-dimension based techniques, on both artificial and real data sets. Robust behavior in the presence of noise, and independence of the spectral dimensionality, is demonstrated. Furthermore, due to its construction, the algorithm can be used for non-linear mixing models as well.
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