By Topic

Application of an object-oriented feature extraction method to quantitative estimation from hyper-spectral 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
$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

3 Author(s)
Fujimura, S. ; Graduate Sch. of Eng., Tokyo Univ., Japan ; Yonenaga, A. ; Kiyasi, S.

Extracting significant features is essential for processing, storing and/or transmission of a vast volume of hyperspectral data. Conventional ways of extracting features are not always satisfactory for this kind of data in terms of optimality and computation time. The authors have already developed an object-oriented feature extraction method designed for supervised classification. They apply the basic idea of the approach to feature extraction for quantitative estimation from hyperspectral data. After the data obtained for various values of a quantity are orthogonalized and reduced by principal component analysis, the features describing the variation of spectra are extracted as linear combinations of the reduced components. An experiment using pigment shows that the feature extraction method for quantitative analysis yielded satisfactory results

Published in:

Geoscience and Remote Sensing Symposium Proceedings, 1998. IGARSS '98. 1998 IEEE International  (Volume:2 )

Date of Conference:

6-10 Jul 1998