Per Pixel Contextual Information for Classification of VHR Images of Urban Areas

Very high spatial resolution satellite images allow to identify individual man-made objects. However, automatic extraction of these objects is still very difficult, especially in urban areas. Spectral information is insufficient to separate the different man-made object classes. Therefore, there is an increased interest in incorporating shape and contextual information in the classification process. Object-based approaches provide a straightforward method to incorporate both shape and contextual information. However, these approaches require a segmentation of the image, which is a very difficult and sensitive task, especially in urban areas. Recently some attempts have been made to incorporate shape information on a pixel basis. In this paper we further develop this approach and propose a method to also derive contextual information on a pixel basis. The per-pixel features developed in this paper contain information about the distance from the object the pixel belongs to to the nearest shadow object. Clearly, this information can be helpful to identify buildings, which are generally accompanied by shadow. However, the proposed method can also be used to describe other contextual information.