BBAuthors: Xiao, J.; Gerke, M.; Advisor: -; Participants: - (2013)
In this paper we develop and compare two methods for scene classification in 3D object space, that is, not single image pixels get classified, but voxels which carry geometric, textural and color information collected from the airborne oblique images and derived products like point clouds from dense image matching. One method is supervised, i.e. relies on training data provided by an operator. We use Random Trees for the actual training and prediction tasks. The second method is unsupervised, thus does not ask for any user interaction. We formulate this classification task as a Markov-Random-Field problem and employ graph cuts for the actual optimization procedure. Two test areas are used to test and evaluate both techniques. In the Haiti dataset we are confronted with largely destr... 
