Scale-dependent Adaptation of Image Analysis Models Incorporating Local Context Objects

Local context objects have significant impact on object extraction from real aerial or satellite images, since occlusions or shadows can substantially hinder a successful extraction of a particular landscape object one is interested in. This paper presents an adaptation concept for image analysis object models considering local context objects to a lower image resolution. The scale-dependent adaptation of local context poses a severe problem for an unambiguous scale behaviour prediction, as the exact position of local context objects is generally unknown. However, by adapting the object models for the landscape object of interest and the local context objects separately, the influence of this contradiction is avoided while the loss of the exact combined scale behaviour prediction remains acceptable. An example for road extraction with vehicle as local context object demonstrates the capability of the adaptation approach. To conclude, the assumptions for the adaptation methods and its limitations are discussed.