Segmentation by Classification for Through-the-Wall Radar Imaging Using Polarization Signatures

Abstract

A scheme for target detection using segmentation by classification is proposed. The scheme is applied to through-the-wall microwave images obtained using frequency-domain back-projection in a wideband radar. We consider stationary targets where Doppler and change-detection-based techniques are inapplicable. The proposed scheme uses features from polarimetric images to segment and classify the image observations into target, clutter, and noise segments. We map target polarization signatures from copolarized and cross-polarized target returns to a pixel-by-pixel feature space, then oversegment the image to homogeneous regions called superpixels depending on this feature space. The features of each superpixel are used subsequently to group homogeneous superpixels into clusters. The clusters are then classified using decision trees. Real data collected using an indoor radar imaging scanner are used for performance validation.