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We present a monoscopic algorithm to detect buildings in small aerial images. After recoding the original picture, we try to match a given number of graphs. Depending on the level of matching, the given picture is classified as building or background. The graphs are constructed based on a learning set and using an entropy criterion to separate building images and background images by recursive partitioning. In the future we hope to extend our algorithm to full-scale aerial images.

We present new experiments on tracking roads from SPOT satellite images. The principle of the algorithm is as follows: we choose “tests” (matched filters for short road segments) one at a time in order to remove as much uncertainty as possible about the road position given the results of the previous tests. The tests are chosen based on a statistical model for the joint distribution of tests and road positions. On-line, we alternate between data collection and optimization: at each iteration new image data is examined and a minimization problem is solved, 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 track in real time mountain highways in southern France over large distances without manual intervention.