Automatic inference of elevation and drainage models from a satellite image

Oblique illumination of irregular topography generates a pattern of highlighting and shadow, as the solar beam directly illuminates those slopes that face the sun while those on opposite sides of ridgelines are shadowed. On remotely sensed images these patterns appear as alternating dark and bright regions that reveal approximate positions of ridges and valleys. Knowledge of scene-specific variables (such as sun angle and elevation), general knowledge of geomorphology, atmospheric scattering, and spectral characteristics of landscapes permits reconstruction of the topography from its manifestation on the image. Raw image data record combined effects of topography, atmosphere, and diverse spectral reflections of surface materials. Our interpretation procedure isolates these several effects. From varied brightnesses caused by direct and indirect illumination, positions of ridges and valleys can be approximated. From variations in material reflectance, large rivers (channels with large areas of open water) can be detected. Finally, relative elevations can be estimated from analysis of drainage and ridge patterns using a strategy of "elevation growing" that assigns increasing elevation values to pixels as they are positioned at greater distances from rivers or other valley pixels already assigned elevations. From the estimated topographic elevations, it is possible to derive a network of drainage channels. Each stream segment in this network is labeled with information pertaining to its length, junction with other segments, direction of flow, and other properties. We then examine this network to detect logical inconsistencies in the labeling of stream segments, then apply a procedure that identifies the optimal labeling to yield the smallest error within the network.