Consistent modeling of terrain and drainage using deformable models

Abstract

We propose an automated approach to modeling drainage channels—and, more generally, linear features that lie on the terrain—from multiple images, which results not only in high-resolution, accurate and consistent models of the features, but also of the surrounding terrain. In our specific case, we have chosen to exploit the fact that rivers flow downhill and lie at the bottom of local depressions in the terrain, valley floors tend to be “U” shaped, and the drainage pattern appears as a network of linear features that can be visually detected in single gray-level images.

Different approaches have explored individual facets of this problem. Ours unifies these elements in a common framework. We accurately model terrain and features as 3-dimensional objects from several information sources that may be in error and inconsistent with one another. This approach allows us to generate models that are faithful to sensor data, internally consistent and consistent with physical constraints. We have proposed generic models that have been applied to the specific task at hand—river delineation and data elevation model (DEM) refinement—and show that the constraints can be expressed in a computationally effective way and, therefore, enforced while initializing the models and then fitting them to the data. We will also argue that the same techniques are robust enough to work on other features that are constrained by predictable forces.

This work was conducted at SRI International, Menlo Park, CA and supported in part by contracts from the Defense Advanced Research Projects Agency.