UPDATING GIS BY OBJECT-BASED CHANGE DETECTION P. Lohmann, P. Hofmann, S. Müller Institute of Photogrammetry and GeoInformation (IPI), Leibniz Universität Hannover (LUH), Nienburger Str. 1, 30167 Hannover, Germany, mailto:lohmann@ipi.uni-hannover.de KEY WORDS: Change detection, change indications, update landuse, landcover ABSTRACT: Today with the situation of rapidly emerging of high resolution earth observation data by optical and microwave sensors there is a growing need for efficient methods to derive, maintain and revise land cover data at various scales by regional, national and European authorities. Main drivers are recent and upcoming European directives and initiatives, which contain increasingly spatially differentiated monitoring and reporting obligations in agriculture, environmental protection and planning, water management and soil protection. This paper focusses on part of a current research project named DeCOVER, namely the change detection which is used to identify candidates of change in land-use (LU) or land-cover (LC). Within this project a consortium of eleven partners with co-funding from the Federal Ministry of Economics and Technology via the German Aerospace Center (DLR) an attempt is being made to develop and demonstrate an innovative and cost-efficient geo- information service aiming in the establishment of a national landcover data base to serve different monitoring and reporting obligations of official users to the EC in the fields of agriculture, environment protection, water management, soil protection or spatial planning. This data base is intended to serve with its structure as a GIS source for ontology-based semantic interoperable methods and data to meet also the requirements of other national data bases like ATKIS, CORINE CLC and BNTK. The planned implementation of DeCOVER includes a concept for an object-based change detection attempt to efficiently update existing geo-spatial data which is described in this paper. The change information needed is derived from recent satellite images using automatic image processing and analysis. The conceptual idea includes both, manual (applying visual interpretation) and automatic image analysis steps resulting in a change layer, which highlights those areas or objects which are suspect for change and gives an indication of the direction of change. This investigation uses multitemporal remote sensing satellite data of a spatial resolution of approximate 5 m to be comparable to the planned German RapidEye system and the existing TerraSAR-X data. Different pre-processing steps have been implemented in order to avoid seasonal effects or changes due to different imaging conditions, such as atmospheric conditions, different sun angles, etc.. However not always ideal imaging conditions can be found which result in change indications, like shadows which becomes more dominant with increasing image resolution. Further pre-processing includes an automatic haze reduction and a shade correction using an appropriate DTM. Image co-registration and automatic cloud and shade-of-clouds detection is performed. Additionally, a priori knowledge of potential change for the object classes used in the GIS of concern is used as input to control the subsequent image processing. The concept of the change detection starts by setting up a focusing step to selectively initiate the following steps only for those objects which are considered as changed. Thereafter all changed objects are classified either visually (manually) or by an automatic procedure depending on the type of change detected. The decision which classification procedure is used depends on a transition-probability-matrix which indicates for each class the degree of likelihood of possible and impossible class-transitions respectively in combination with a table of available classification operators which can be applied to validate the predicted change. The transition-probability-matrix is generated manually and contains assumed possible changes from one class to another. If an automatic classification is indicated, the procedure then consists of two parts: First it is evaluated if the object’s geometry is changed or if the object is changed as a whole. If a change in geometry is detected, the object of concern has to be re-segmented and re-classified. If not, the object has to be re-classified only. If a manual classification is indicated, changes will be mapped respectively. At the end the results of the visual/manual classification and the automatic classification are joined into one change layer, which holds for each changed object besides its change indication information, the objects’ historical classification and its new classification. This layer can then be directly used as input for updating existing GIS databases. This paper concentrates on the first part of the process chain, namely the focusing module. The focusing module has two tasks: First, objects have to be found in the GIS data which are affected by change. Second, the focusing module has to decide, whether the changed objects subsequently can be processed automatically or must be processed manually. Different change indicators are implemented based on a comparison of the input satellite data of two different dates. These indicators in combination with a transition-probability-matrix are used to limit the new possible classes and control the subsequent re-cursive processing and use of operators to verify the indicated changes according to the sorted probabilities of change. The obtained results of the proposed object-based change detection process chain are compared to change-detection results obtained by completely visual interpretation. Finally all results are assembled to a resultant change indication map.