Abstract
Ultra-wideband synthetic aperture radar (UWB SAR) is a su-cient approach to detect landmines over large areas from a safe standofi distance. Feature extraction is the key step of landmine detection processing. On the one hand, the feature vector should contain more scattering characteristics to discriminate landmines from clutters; on the other hand, the dimensionality of feature vector should be lower to avoid the \curse of dimensionality. In this paper, a novel feature vector extraction method is proposed. We flrst obtain the scattering characteristics in four domains, i.e., range, azimuth, frequency and aspect-angle, via the space-wavenumber distribution (SWD). Since the data after SWD are with higher dimension and local nonlinear structures, a typical manifold learning method, Isomap, is used to reduce the dimension. The validity of the proposed method is proved by using the real data collected by an airship-borne UWB SAR system.
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