Remotely sensed images and GIS data fusion for automatic change detection

Abstract

During the past years, researchers have put forward a large number of change detection techniques for using remotely sensed images and have summarised them from different viewpoints. However, most research has mainly focused on images versus images and 2D change detection; the detection results have been imprecise especially as altitude changes cannot be detected because of the lack of data. As multi-source data can be acquired more and more easily, change detection with multi-source data has become a hot issue. Because change detection with multi-source data can eliminate the effects of the atmosphere and topography and improve the ability to identify and extract objects, more accurate results can be obtained from change detection procedures. This article aims to integrate GIS data with images into applications of change detection. Change detection of linear, area and terrain features based on multi-source data is investigated and change detection based on artificial neural networks (ANN) and GIS data is also analysed. As the powerful GIS functions provide efficient tools for multi-source data processing and change detection analysis, we can expect more research taking this approach as a generic trend in change detection.

Keywords:

• change detection
• GIS
• data fusion
• buffer detection
• ANN

Acknowledgements

The work described in this article was supported by Dr H.G. Sui, Dr X.D. Zhang, Dr P. Xiao and Dr S. Xia during their doctoral studies with me and funded by the National Natural Fund of China (NSFC) (No. 60602013) and the National Key Fundamental Research Plan of China (973) (No. 2006CB701300).