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Detecting smaller elevated objects, like chimneys, in high resolution images has several important applications, such as collision warning. On the other hand, the already existing 3D models (that already include the terrain, buildings and vegetation) can be enriched by new instances. There are not many contributions about extracting fine roof details in the literature. Therefore, we developed a new, modularized algorithm for detecting these details as hot spots in the local elevation maps; such a map is typically obtained by a multi-view dense matching method. We use explicit and implicit assumptions on data in order to tighten the search range for chimneys and reduce the number of false alarms. Finally, filtering hot spots by means of color or intensity images takes place. Thus, good detection rates can be achieved for a data set consisting of several high resolution images taken over a residential area.

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