Improving Spatiotemporal Change Detection: A High Level Fusion Approach for Discovering Uncertain Knowledge from Satellite Image Database

Abstract—This paper investigates the problem,of tracking spa- tiotemporal changes of a satellite image through the use of Knowl- edge Discovery in Database (KDD). The purpose of this study is to help a given user effectively discover interesting knowledge,and then build prediction and decision models. Unfortunately, the KDD process for spatiotemporal data is always marked,by several types of imperfections. In our paper, we take these imperfections into consideration in order to provide more accurate decisions. To achieve this objective, different KDD methods are used to discover knowledge in satellite image databases. Each method presents a different point of view of spatiotemporal evolution of a query model (which represents an extracted object from a satellite image). In order to combine these methods, we use the evidence fusion theory which considerably improves,the spatiotemporal knowledge,discovery process and in- creases our belief in the spatiotemporal model change. Experimental results of satellite images representing the region of Auckland in New Zealand depict the improvement,in the overall change,detection as compared,to using classical methods. Keywords—Knowledge discovery in satellite databases, knowledge