Cart (Loading....) | Create Account
Close category search window

Crisp and Fuzzy Adaptive Spectral Predictions for Lossless and Near-Lossless Compression of Hyperspectral Imagery

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

4 Author(s)
Aiazzi, B. ; CNR Area della Ricerca di Firenze, Florence ; Alparone, L. ; Baronti, S. ; Lastri, C.

This letter presents an original approach that exploits classified spectral prediction for lossless/near-lossless hyperspectral-image compression. Minimum-mean-square-error spectral predictors are calculated, one for each small spatial block of each band, and are classified (clustered) to yield a user-defined number of prototype predictors that are capable of matching the spectral features of different classes of pixel spectra for each wavelength. Such predictors are used to achieve a prediction, either crisp or fuzzy. Unlike most of the methods reported in the literature, the proposed approach exploits a purely spectral prediction that is suitable in compressing the data in band-interleaved-by-line format, as they are available at the output of the onboard instrument. In that case, the training phase, i.e., clustering and refining of predictors for each wavelength, may be moved offline. Experimental results on Airborne Visible InfraRed Imaging Spectrometer data show improvements over the most advanced methods in the literature, with a computational complexity that is far lower than that of analogous methods by the same and other authors.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:4 ,  Issue: 4 )

Date of Publication:

Oct. 2007

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.