Cart (Loading....) | Create Account
Close category search window
 

Knowledge based approach for automated digital image processing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Inampudi, R.B. ; Dept. of Comput. Sci.& Eng., Nagarjuna Univ., Guntur, India ; Guntupalli, S.P. ; Rao, A.A.

Automatic interpretation of digital images is a difficult task. In this paper, we have proposed knowledge based approach for Landsat image segmentation and interpreted without any prior image dependent information. It includes an integration of image processing techniques, knowledge from domain experts and ancillary information such as previous maps of the study area. We discuss three important issues in automatic digital image interpretation are image registration, road detection and knowledge based segmentation. In this study, major land cover types are organized in a hierarchical structure. A complete knowledge based segmentation technique may consist of two stages. The first stage uses the proposed method to segment a Landsat image by spectral knowledge rules. The second stage then collects more area dependent spatial rules and the prior map information to perform a complete segmentation. Nagao and Mastuyama have developed a knowledge-based system, which performs a structural analysis of complex aerial photographs using a technique called segmentation-by-recognition. One advantage of this method is its flexibility in applying it to geo graphically different areas. This work develops techniques for automating the process of IRS LISS-III image interpretation. The experimental results show that the proposed method can be successful in segmenting complicated Landsat image. Through this study, we believe that the proposed hierarchical method is a promising approach for Landsat image segmentation.

Published in:

Geoscience and Remote Sensing Symposium, 2002. IGARSS '02. 2002 IEEE International  (Volume:3 )

Date of Conference:

2002

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.