A method of content-based image analysis using SVM classifier in data catalogue and archive system of remote sensing satellite

XiuLi Wang; ShiMing Fan

doi:10.1117/12.855483

2 April 2010 A method of content-based image analysis using SVM classifier in data catalogue and archive system of remote sensing satellite

XiuLi Wang, ShiMing Fan

Author Affiliations +

Proceedings Volume 7651, International Conference on Space Information Technology 2009; 76510T (2010) https://doi.org/10.1117/12.855483
Event: International Conference on Space Information Technology 2009, 2009, Beijing, China

Abstract

To automatic analysis of the image content,an architecture of content-based image analysis component is designed, which is compatible with existing remote sensing images catalogue and archive system. Thus, indexing can be generated on images content for the images archive. An learning algorithm relying on support vector machine (SVM) is also proposed to classify and label the region of interest in high resolution remote sensing images.This algorithm needs a small size and consumes little computing time online. At the same time, the algorithm can automatically generate semantic interpretations from images.

Citation Download Citation

XiuLi Wang and ShiMing Fan "A method of content-based image analysis using SVM classifier in data catalogue and archive system of remote sensing satellite", Proc. SPIE 7651, International Conference on Space Information Technology 2009, 76510T (2 April 2010); https://doi.org/10.1117/12.855483

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