By Topic

Object-oriented method of land cover change detection approach using high spatial resolution remote sensing data

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
$33 $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)
Li Jia-Cun ; Inst. of Remote Sensing Applications, Chinese Acad. of Sci., Beijing, China ; Qian Shao-Meng ; Chen Xue

Several land cover change detection approaches have been developed such as traditional post-classification cross-tabulation, cross-correlation analysis, neural networks and knowledge-based expert systems. But the procedures for analyzing remote sensing image have relied upon pixel-based methods. So, these methods are restricted by the spatial resolution of remote sensing image and widely used with moderate resolution image. With the advent of high spatial resolution remote sensing data, this situation is different. The characteristics of high spatial resolution remote sensing image are different essentially with these of moderate resolution image. So, these traditional methods are not applicable with the new data. In order to solve the problems and make full use of the advantages of high spatial resolution data, an object-oriented land cover change detection approach was developed in this paper. The whole image was segmented into several objects by texture characteristics and relationship between each pixel. Then, a supervised classification was carried out at the object level. The change detection was also realized by the comparison between images of different times.

Published in:

Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International  (Volume:5 )

Date of Conference: