By Topic

A neural network based approach to detecting urban land cover changes using Landsat TM and IKONOS imagery

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)

Much attention has been drawn to the new applications and opportunities afforded by high-resolution satellite imagery, such as IKONOS and QuickBird. The purpose of this paper is to examine the extent to which high-resolution change detection can be performed using a combination of high and medium-resolution satellite imagery. This combination is important for detecting changes during the time before and after the high-resolution satellite imagery was made available. In particular, the analysis is oriented towards smaller cities and municipalities. Many change detection algorithms and methods have been evaluated. The post-classification change detection algorithm was deemed to be the most suitable technique for this project. Landsat 5 TM and IKONOS MS images of Fredericton, New Brunswick, Canada, were used as source data for the change detection. The results tend to suggest that it is possible to extract reliable change detection information pertaining to small streets, and new rows of residential housing with a medium-resolution benchmark. However, the detection of change in individual houses and small buildings proved to be beyond the capabilities of this procedure.

Published in:

Remote Sensing and Data Fusion over Urban Areas, 2003. 2nd GRSS/ISPRS Joint Workshop on

Date of Conference:

22-23 May 2003