Fusion of hyperspectral images and height models using edge probability

We propose a method for data fusion of hyperspectral images (HSI) and digital surface models (DSM) basing on the edge probabilities from both datasets. A height discontinuity in DEM and change in material in HSI represent the high probability of an edge. Edge probabilities are computed in scale-space and combined according to the Gaussian mixture model. The reliability of the datasets can be included into this model as a prior knowledge. The method is tested on an urban area, where building boundaries represent the high probabilities of an edge in both datasets. Our results show, that the probabilistic fusion technique is advantageous where boundary detected from only one dataset are unreliable.