Share Email Print

Proceedings Paper

Quantitative analysis on lossy compression in remote sensing image classification
Author(s): Yatong Xia; Zimeng Li; Zhenzhong Chen; Daiqin Yang
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In this paper, we propose to use a quantitative approach based on LS-SVM to perform estimation of the impact of lossy compression on remote sensing image compression. Kernel function selection and the model parameters computation are studied for remote sensing image classification when LS-SVM analysis model is establish. The experiments show that our LS-SVM model achieves a good performance in remote sensing image compression analysis. Classification accuracy variation according to compression ratio scales are summarized based on our experiments.

Paper Details

Date Published: 4 March 2015
PDF: 8 pages
Proc. SPIE 9410, Visual Information Processing and Communication VI, 94100K (4 March 2015); doi: 10.1117/12.2083205
Show Author Affiliations
Yatong Xia, Wuhan Univ. (China)
Zimeng Li, Wuhan Univ. (China)
Zhenzhong Chen, Wuhan Univ. (China)
Daiqin Yang, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 9410:
Visual Information Processing and Communication VI
Amir Said; Onur G. Guleryuz; Robert L. Stevenson, 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?