Large-scale Aerial Image Interpretation Using a Redundant Semantic Classification

This work introduces an efficient classification pipeline, which provides an accurate semantic interpretation of urban environments by using redundant scene observations. The image-based method integrates both appearance and height data to classify single aerial images. Given the initial classification of highly overlapping images, a projection to a common orthographic 3D world coordinate system provides redundant observations from multiple viewpoints and enables a semantic interpretation of large-scale urban environments. In the experimental evaluation we investigate how the use of redundancy influences the accuracy in terms of correctly classified pixels for object classes like building, tree, grass, street and water areas. Moreover, we exploit an efficient yet continuous formulation of the Potts model to obtain a consistent labeling of the pixels in the orthographic view. We present results for the datasets Dallas and Graz.