URBAN EXPANSION MONITORING USING SATELLITE IMAGES BY MEANS OF DECISION LEVEL FUSION OF FUZZY CHANGE DETECTORS
This study investigates urban expansion using satellite images by means of decision level fusion of two different fuzzy change detectors. The aim of this study is taking the advantage of effects of various change detection techniques. These techniques are differentiated by the way they use the dataset which their fusion could lead us to gain complementary outputs and ultimately achieve higher accuracy in change detection. This study focuses on the use of this framework specifically for urban region expansion monitoring. Here outputs of two fuzzy change detectors based respectively on post classification comparison and spectral-temporal combined analysis methods of multi-temporal ASTER images are fused by using Sugeno fuzzy integral operator. Accuracy assessment that has been done by available land cover maps for fuzzy post classification comparison combined fuzzy spectral–temporal analysis and their combination technique has shown improvement of change detection accuracy over each single fuzzy change detector in urban expansion application.