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We analyze if the presence of corners in Very High Resolution (VHR) satellite images can give us an indication on the type of structure present in a scene (man-made versus natural structures). Two the corner detectors are validated in this respect: Harris and SUSAN. The detection performance is evaluated over a spectrum of spatial resolutions for current and future VHR systems (from 2 meters to 17 centimeters). The ground truth of this study consists of annotated image extracts containing different types of man-made structures, in which the relevant corners have been identified.

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A method is explored to assess the quality of road network data based on image information in a reliable and accurate way. In the field of geography, an accuracy assessment method, called buffer-overlay-statistics, is known to assess the spatial quality of a line data set by using another line data set of higher spatial accuracy. Here, the method is adapted to assess the quality of a line data set based on image information rather than vector data. The average displacement accuracy measure is redefined, such that it is able to take into account line detection errors (fragmentation and noise). Experiments are conducted on artificial data showing how road extraction out of very high resolution satellite images can be used to asses the spatial accuracy of an existing road vector database.