An active testing model for tracking roads in satellite images

We present a new approach for tracking roads from satellite images, and thereby illustrate a general computational strategy (''active testing'') for tracking 1D structures and other recognition tasks in computer vision. Our approach is related to recent work in active vision on ''where to look next'' and motivated by the ''divide-and-conquer'' strategy of parlor games such as ''twenty Questions''. We choose ''tests'' (matched filters for short road segments) one at a time in order to remove as much uncertainty as possible about the ''true hypothesis'' (road position) given the results of the previous tests. The tests are chosen on-line based on a statistical model for the joint distribution of tests and hypotheses. The problem of minimizing uncertainty (measured by entropy) is formulated in simple and explicit analytical terms. To execute this entropy testing rule we then alternate between data collection and optimization: At each iteration new image data are examined and a new entropy minimization problem is solved (exactly), resulting in a new image location to inspect, and so forth. We report experiments using panchromatic SPOT satellite imagery with a ground resolution of ten meters: Given a starting point and starting direction, we are able to rapidly track highways in southern France over distances on the order of one hundred kilometers without manual intervention.