Pixel-based classification algorithm for mapping urban footprints from radar data: a case study for RADARSAT-2

Kurzfassung

The process of rapid urbanization is attended by various adverse effects on the society, the ecology, and the economy. Effective urban and regional planning is the key to minimizing these effects and the impact on the people who live in cities. For this reason, detailed geospatial information on urban dynamics is needed, which can help to analyze and understand the process of urbanization. Different studies have shown that high-resolution radar imagery is an excellent basis for classifying, monitoring, and analyzing the outline and spatio-temporal development of urban agglomerations. Specifically, the analysis of texture information based on local speckle characteristics has shown its ability to generate large area urban maps. In this study a pixel-based, fully automatic classification approach developed for TerraSAR-X StripMap data was transferred and applied to five RADARSAT-2 images acquired in ultra-fine mode. The algorithm was applied to data from the cities of Mannheim and Ludwigshafen and their rural surroundings in Germany. The classification approach was validated for its multisensoral transferability and robustness. A relative comparison to a TerraSAR-X classification and an absolute comparison to a reference dataset show promising results and allow for the conclusion that the methodology can be fully applied to RADARSAT-2 data.