UNCERTAIN TRAINING DATA EDITION FOR AUTOMATIC OBJECT-BASED CHANGE MAP EXTRACTION

Abstract. Due to the rapid transformation of the societies, and the consequent growth of the cities, it is necessary to study these changes in order to achieve better control and management of urban areas and assist the decision-makers. Change detection involves the ability to quantify temporal effects using multi-temporal data sets. The available maps of the under study area is one of the most important sources for this reason. Although old data bases and maps are a great resource, it is more than likely that the training data extracted from them might contain errors, which affects the procedure of the classification; and as a result the process of the training sample editing is an essential matter. Due to the urban nature of the area studied and the problems caused in the pixel base methods, object-based classification is applied. To reach this, the image is segmented into 4 scale levels using a multi-resolution segmentation procedure. After obtaining the segments in required levels, training samples are extracted automatically using the existing old map. Due to the old nature of the map, these samples are uncertain containing wrong data. To handle this issue, an editing process is proposed according to K-nearest neighbour and k-means algorithms. Next, the image is classified in a multi-resolution object-based manner and the effects of training sample refinement are evaluated. As a final step this classified image is compared with the existing map and the changed areas are detected.