By Topic

Fuzzy Markov chains approach to feature selection for high dimensional remote sensing data

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
$33 $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)
Shixin Yu ; Dept. of Phys., Antwerp Univ., Belgium ; Scheunders, P.

Advances in sensor technology for Earth observation make it possible to collect multispectral data in much high dimensionality. For example, the NASA/JPL Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) generates image data in more than 220 spectral bands simultaneously. For such high dimensionality, the appropriate selection of features has a significant effect on the cost and accuracy of an automated classifier. In this paper, a feature selection method using fuzzy Markov chains is proposed. It has been shown that the fuzzy Markov chain is a robust system with respect to small perturbations of the transition matrix, which is not the case for the classical probability Markov chains. In this paper, classical and fuzzy Markov chain approaches are applied to the problem of feature selection for high dimensionality

Published in:

Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International  (Volume:7 )

Date of Conference: