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Proceedings Paper

Object-oriented classification of remote sensing data for change detection
Author(s): Yang Chen; Ying Chen; Yi Lin
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Paper Abstract

This paper introduces a method regarding the remote sensing data for change detection by using GIS database. The concept of object-oriented has been used in this method to classify the remote sensing data. The objects of the classification not only can be single pixels of image but also can be pixel sets that represent GIS objects. The remote sensing data are classified with a supervised maximum likelihood classification. In order to reduce the workload and avoid the dependence on operator's experiences, the training areas are generated from the GIS database. Experiments show the method is effective on detecting the change of area objects.

Paper Details

Date Published: 28 October 2006
PDF: 9 pages
Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 64191J (28 October 2006); doi: 10.1117/12.713253
Show Author Affiliations
Yang Chen, Tongji Univ. (China)
Ying Chen, Tongji Univ. (China)
Yi Lin, Tongji Univ. (China)


Published in SPIE Proceedings Vol. 6419:
Geoinformatics 2006: Remotely Sensed Data and Information
Liangpei Zhang; Xiaoling Chen, Editor(s)

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