Object-oriented classification of remote sensing data for change detection

Yang Chen; Ying Chen; Yi Lin

doi:10.1117/12.713253

28 October 2006 Object-oriented classification of remote sensing data for change detection

Yang Chen, Ying Chen, Yi Lin

Proceedings Volume 6419, Geoinformatics 2006: Remotely Sensed Data and Information; 64191J (2006) https://doi.org/10.1117/12.713253
Event: Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 2006, Wuhan, China

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.

Citation Download Citation

Yang Chen, Ying Chen, and Yi Lin "Object-oriented classification of remote sensing data for change detection", Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 64191J (28 October 2006); https://doi.org/10.1117/12.713253

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