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

Segmentation of multispectral remote-sensing images based on Markov random fields

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

2 Author(s)
Iong-Wen Tsai ; Inst. of Comput. Sci. & Electron. Eng., Nat. Central Univ., Chung-Li, Taiwan ; Din-Chang Tseng

An unsupervised approach for texture segmentation of multispectral remote-sensing images based on Gaussian Markov random fields (GMRFs) is proposed. At first, the authors treat the false-color information of SPOT satellite images as RGB attributes and then transform them to HSI attributes. Secondly, a scale-space filter is used to threshold the hue histogram to quantize the color set which represents the principal color components in the original image. Thirdly, the global GMRF parameters are estimated from the original image for global segmentation. Fourthly, they label each pixel of the image based on the quantized color set and the GMRF parameters to maximize a posterior color distribution probability to achieve the global segmentation. Fifthly, a criterion is used to judge whether every pixel in the global-segmented image is within a local textured region or not. Finally, the pixels in a local textured region are further estimated the local GMRF parameters and clustered based on the parameters. Seven SPOT images were segmented to demonstrate the ability of the proposed approach. Moreover, the scale-space filter, the MRF-based global segmentation, and the pure local (texture) parameter classification are sequentially evaluated their performance

Published in:

Geoscience and Remote Sensing, 1997. IGARSS '97. Remote Sensing - A Scientific Vision for Sustainable Development., 1997 IEEE International  (Volume:1 )

Date of Conference:

3-8 Aug 1997

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.