Using radar in a through-the-wall imaging application is an expanding field of research both for civilian and
military uses. Thus far, most of the attention has been directed toward building radar imaging systems to detect
objects within a room or building. The resulting images are full of ambiguity and difficult to interpret what the
image is displaying. Presented here is a novel approach that addresses the interpretation of the images produced
by the aforementioned imaging systems. We propose a classification scheme that provides an interpretation
of an urban environment imaged in 3D. This approach builds probabilistic object models from feature vectors
extracted from a volumetric radar image. A minimum-distance classifier is used to label radar image data and
provide a 3D visualization of an urban scene. Results using real radar backscatter data validate the effectiveness
of our method.
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