Hierarchical shadow detection for color aerial images
A hierarchical shadow detection algorithm for color aerial images is presented in this paper to meet two challenges for static shadow detection in the literature: different brightness and illumination conditions in different images and the complexity of aerial images. The hierarchical algorithm consists of two levels of processing: the pixel level classification, achieved through modelling an image as a reliable graph (RG) and maximizing the graph reliability using the EM algorithm, and the region level verification, achieved through minimizing the Bayesian error by further exploiting the domain knowledge. Further analyses show that MRF model based segmentation is a special case of the RG model. The relationship between the RG model and the relaxation labeling model is also discussed. A quantitative comparison between this method and a state-of-the-art shadow detection algorithm clearly indicates that this method is promising for delivering effective shadow detection performance under different illumination and brightness conditions.
shadow detection aerial image static shadow reliable graph em algorithm relaxation labelling image segmentation supervised classification markov random field