By Topic

Unsupervised change detection on remote sensing images using non-local information and Markov Random Field Models

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

5 Author(s)
Peng Liu ; Center for Earth Obs. & Digital Earth, China ; Shengtao Sun ; Guoqing Li ; Jibo Xie
more authors

In this paper, inspiring by the idea of non-local means filter, the non-local information is introduced into the Markov Random Field Models (MRF) based change detection. A new distance based on non-local information of the neighborhood area of remote sensing image is defined. Then the image information is map to a higher dimension feather space. The initial cluster classification is performed in the high dimension non local space. And it provides the initial value for the MRF change detection. Both the data term and the smoothing term in the MRF based change detection are defined in this frame of non-local information. Different multi-temporal images with different resolutions and different locations are experimented. And better performances are achieved in the experiments when comparing with two other method.

Published in:

Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International

Date of Conference:

22-27 July 2012