Share Email Print

Proceedings Paper

Classification by using wavelet transform on multispectral images
Author(s): Hai-Hui Wang; Ai-Ping Cai
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

This study analyzed texture features in multi-spectral image data. Recent development in the mathematical theory of wavelet transform has received overwhelming attention by the image analysts. An evaluation of the ability of wavelet transform and other texture analysis algorithms in feature extraction and classification was performed in this study. The algorithms examined were the wavelet transform, spatial co-occurrence matrix, fractal analysis, and spatial autocorrelation. The performance of the above approaches with the use of different feature was investigated. Wavelet transform was found to be far more efficient than other advanced spatial methods.

Paper Details

Date Published: 28 October 2006
PDF: 7 pages
Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 64191R (28 October 2006); doi: 10.1117/12.713266
Show Author Affiliations
Hai-Hui Wang, Wuhan Institute of Technology (China)
Ai-Ping Cai, Wuhan Institute of Technology (China)

Published in SPIE Proceedings Vol. 6419:
Geoinformatics 2006: Remotely Sensed Data and Information
Liangpei Zhang; Xiaoling Chen, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?