Yu, Jun
- Department of Forest Economics, Swedish University of Agricultural Sciences
Abstract
This work presents methods for multispectral image classification using the discrete wavelet transform. Performance of some conventional classification methods is evaluated, through a Monte Carlo Study, with or without using the wavelet transform. Spatial autocorrelation is present in the computer-generated data on different scenes, and the misclassification rates are compared. The results indicate that the wavelet-based method performs best among the methods under study. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keywords
contextual classification; wavelet; spatial autocorrelation; multispectral imagery; Monte Carlo study; remote sensing
Published in
Pattern Recognition
2003, volume: 36, number: 4, pages: 889-898
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
SLU Authors
Ekström, Magnus
- Department of Forest Economics, Swedish University of Agricultural Sciences
- Department of Forest Economics, Swedish University of Agricultural Sciences
UKÄ Subject classification
Probability Theory and Statistics
Publication identifier
- DOI: https://doi.org/10.1016/S0031-3203(02)00125-5
Permanent link to this page (URI)
https://res.slu.se/id/publ/4983