Feature extraction in through-the-wall radar imaging.

This paper deals with the problems of automatic target classification of Through-the-Wall radar imaging. The proposed scheme considers stationary objects in enclosed structures and works on the SAR image rather than the raw data. It comprises segmentation, feature extraction based on superquadrics, and classification. We present a recursive splitting tree to obtain optimum parameters for feature extraction. Support vector machines and nearest neighbor classifiers are then applied to successfully classify among different indoor targets. The classification methods are tested and evalutated using real data generated from synthetic aperture Through-the-Wall radar imagining experiments.

Main Author: Debes, Christian.
Other Authors: Hahn, Jurgen., Zoubir, Abdelhak M., Amin, Moeness G.
Format: Villanova Faculty Authorship
Language: English
Published: 2010
Online Access: http://ezproxy.villanova.edu/login?url=https://digital.library.villanova.edu/Item/vudl:173303