By Topic

Hyperspectral Intrinsic Dimensionality Estimation With Nearest-Neighbor Distance Ratios

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Heylen, R. ; iMinds-Visionlab, Univ. of Antwerp, Wilrijk, Belgium ; Scheunders, P.

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.

Published in:

Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of  (Volume:6 ,  Issue: 2 )