Semantic evaluation of high-resolution low-cost digital camera data for urban classification

Abstract

Many applications of remote sensing - like, for example, urban monitoring - require high resolution image data for a correct determination of object geometry. The desired geometry of an object's surface is created in dieffernet studies by use of well known segmentation techniques. In this study, we evaluate the influence on image quality of analog and digital image data on the results of a image segmentation in eCognition. We compare the suitability of analog middle format camera data with image data produced by a commercial "of the shelf" digital camera taken during two campaigns in 2003 and 2004. Furthermore, the results of a multiresolution classification of an urban test site by use of both datasets will be presented. An outlook for future work on a multiresolution data fusion with hyperspectral data will be given at the end of this paper.