Textural feature based target detection in through-the-wall radar imagery
2013
Stationary target detection in through-the-wall radar imaging (TWRI) using image segmentation techniques has recently
been considered in the literature. Specifically, histogram thresholding methods have been used to aid in removing the
clutter, resulting in ‘clean’ radar images with target regions only. In this paper, we show that histogram thresholding
schemes are effective only against clutter regions, which are distinct from target regions. Target detection using these
methods becomes challenging, if not impossible, in the presence of multipath ghosts and clutter that closely mimics the
target in size and intensity. Because of the small variations between the target regions and such clutter and multipath
ghosts, we propose a textural feature based classifier for through-the-wall target detection. The feature based scheme is
applied as a follow-on step after application of histogram thresholding techniques. The training set consists of feature
vectors based on gray level co-occurrence matrices corresponding to the target and ghost/clutter image regions. Feature
vectors are then used in training a minimum distance classifier based on Mahalanobis distance metric. Performance of
the proposed scheme is evaluated using real-data collected with Defence Research and Development Canada’s vehicle-borne
TWRI system. The results show that the proposed textural feature based method yields much improved results
compared to histogram thresholding based segmentation methods for the considered cases.
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