Comparison of land cover classification using high-resolution TerraSAR-X and optical imagery
Conference: EUSAR 2010 - 8th European Conference on Synthetic Aperture Radar
06/07/2010 - 06/10/2010 at Aachen, Germany
Proceedings: EUSAR 2010
Pages: 4Language: englishTyp: PDFPersonal VDE Members are entitled to a 10% discount on this title
Wegner, Jan Dirk; Nezam, Shoaib; Müller, Sönke; Sörgel, Uwe (Institute of Photogrammetry and GeoInformation (IPI), Leibniz University Hanover, Germany)
TerraSAR-X is capable of acquiring imagery of one meter resolution. In data of such kind man-made objects become visible and typical land cover classification classes appear in high detail. Our aim is to find out if TerraSAR-X imagery may complement optical images for automatic land cover classification. Thus, we classify imagery of both data types into classes settlement, agriculture, streets, and forested areas and compare classification performances. We use Markov Random Fields (MRF) as learning based probabilistic framework to classify optical and SAR data. In case of the TerraSAR-X amplitude data we model the likelihood function with Fisher distributions, whereas texture measures are evaluated using Gibbs probability distributions for the optical images. First results show that high-resolution TerraSAR-X imagery may complement land cover classification.