Quantitative analysis on lossy compression in remote sensing image classification

Yatong Xia; Zimeng Li; Zhenzhong Chen; Daiqin Yang

doi:10.1117/12.2083205

4 March 2015 Quantitative analysis on lossy compression in remote sensing image classification

Yatong Xia, Zimeng Li, Zhenzhong Chen, Daiqin Yang

Proceedings Volume 9410, Visual Information Processing and Communication VI; 94100K (2015) https://doi.org/10.1117/12.2083205
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

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.

Citation Download Citation

Yatong Xia, Zimeng Li, Zhenzhong Chen, and Daiqin Yang "Quantitative analysis on lossy compression in remote sensing image classification", Proc. SPIE 9410, Visual Information Processing and Communication VI, 94100K (4 March 2015); https://doi.org/10.1117/12.2083205

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available