Using spatial concepts to integrate data and information from various sources for a knowledge-based assessment of impervious surfaces

In this study, we present a concept for the assessment of impervious surfaces integrating VHR satellite data and a-priori information from additional datasets. Spatial concepts like neighbourhood and region, distance, spatial dependence or spatial variability are adapted in a knowledge-based approach using an object-based image analysis model to accumulate evidence from different sources. We look at constraints for timely and comprehensive VHR optical data acquisition that covers larger areas with adequate image characteristics (sensor family, seasonality, sensor viewing angles and sun inclination). For a study area covering the municipality of Hallein (Austria), we discuss preliminary results with a focus on real-world object characterization (including surface material, spectral reflectivity, object size and shape) and on building a knowledge-base for the classification of real-world objects. We also assess image characteristics and effects on image analysis. The knowledge about real-world object characteristics and image object statistics will be used to develop an integrated approach that aims for transferability to larger areas.