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The paper deals with change detection using time series SAR images. SAR provides a unique opportunity for detecting land-use changes within short intervals (e.g., monthlyl in tropical and sub-tropical regions with cloud cover. Traditional change detection methods mainly rely on per-pixel spectral information but ignore per-object structural inform ation. In this study, a new method is presented that integrates object-oriented analysis with case-based reasoning (cBR) for change detection. Object-oriented analysis is carried out to retrieve a variety of features, such as tone, shape, texture, area, and context. An incremental segmentation technique is proposed for deriving change objects from multi-temporal Radarsat images. Feature selection based on genetic algorithms is carried out to determine the optimal set of features for change detection. A CBR matching algorithm is developed to identify the temporal positions and the kind of changes. It is based on the weighted k-Nearest Neighbor classification using an accumulative similarity measure. The comparison of the four combinations of change detection methods, object-based or pixel-based plus case-based or rule-based, is carried out to validate the performance of this proposed method. The analysis shows that this integrated approach has provided an efficient way of detecting land-use changes at monthly intervals by using multi-temporal SAR images. [ABSTRACT FROM AUTHOR]