Object-Oriented Classification of Orthophotos to Support Update of Spatial Databases
Interpretation of aerial images is normally carried out by visual interpretation as traditional classification routines are too limited in dealing with the complexity of very high resolution data. Segmentation based classifiers can overcome this limitation by dividing images into homogenous segments and using them as basis for further classification procedures. In this paper this approach is examined in view of its potential to support the update of existing land use data bases. A workflow was developed that allows the classification of high-resolution aerial images, the subsequent comparison with land use data and the assessment of identified changes. Special emphasis is put on the transferabilitY of the procedure in terms of study area as well as image and land use data.