Aerial image segmentation for flood risk analysis

This paper presents a technique for image segmentation. We demonstrate its efficacy for classsifying high-resolution aerial images. The application is peak water flow estimation in a river catchment in the city of Zurich and the data covers a large rural and urban setting. The output of the segmentation process is used as input to a hydrological model. We introduce a combined, probabilistic, segmentation approach based on colour (the LAB colour space is used), texture (using entropy) and image features (gradients). Classification rates for natural land surfaces and man-made structures are up to 90% and 85% respectively. When the automatic segmentation result is compared to the official land use data and reclassified for use in GIS we achieve an overall classification accuracy of 70%. This new classification is tested on the WetSpa hydrological model and the resulting flow estimate compares favourably with that computed from hand-classified land use data.