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. Six change detection procedures were
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 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.