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Abstract
Frequent acquisition of remotely sensed data makes it possible to use the satellite images to determine type and extent of changes in the environment. Many digital change detection algorithms have been developed since the launch of ERTS-1 in 1972 to reveal changes. With the launch of satellite with different sensor characteristics and advancement in mathematical data processing algorithms, the use of new techniques to compare multi-temporal image data in change detection procedure is required. The purpose of this research is to find the most suitable technique from the available techniques of change detection, which can be applied for areas, with similar conditions as the study area.
Seven change detection procedures werew tested for detecting areas of changes in the Assiut city and the surrounding using SPOT XS images. The change detection techniques considered are post-classification comparison, image differencing, image regression, image ratioing, principle component analysis, vegetation indices and change vector analysis. The accuracy of the results obtained by each technique was evaluated by comparing overall accuracy and kappa coefficient. Principle component analysis was found to be the most accurate procedure.
Keywords: Change detection, image processing, classification accuracy.