Textural Feature Selection for Enhanced Detection of Stationary Humans in Through-the-Wall Radar Imagery

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Authors
  1. Chaddad, A.
  2. Ahmad, F.
  3. Amin, M.G.
  4. Sévigny, P.
  5. DiFilippo, D.
Corporate Authors
Defence Research and Development Canada, Ottawa Research Centre, Ottawa ON (CAN);Villanova University, Villanova PA (US) Radar Imaging Lab, Center for Advanced Communications
Abstract
Feature-based methods have been recently considered in the literature for detection of stationary human targets in through-the-wall radar imagery. Specifically, textural features, such as contrast, correlation, energy, entropy, and homogeneity, have been extracted from gray-level co-occurrence matrices (GLCMs) to aid in discriminating the true targets from multipath ghosts and clutter that closely mimic the target in size and intensity. In this paper, we address the task of feature selection to identify the relevant subset of features in the GLCM domain, while discarding those that are either redundant or confusing, thereby improving the performance of feature-based scheme to distinguish between targets and ghosts/clutter. We apply a Decision Tree algorithm to find the optimal combination of co-occurrence based textural features for the problem at hand. We employ a K-Nearest Neighbor classifier to evaluate the performance of the optimal textural feature based scheme in terms of its target and ghost/clutter discrimination capability and use real-data collected with the vehicle-borne multi-channel through-the-wall radar imaging system by Defence Research and Development Canada. For the specific data analyzed, it is shown that the identified dominant features yield a higher classification accuracy, with lower number of false alarms and missed detections, compared to the full GLCM based feature set.
Keywords
through-wall radar imaging;feature selection;target detection;co-occurrence matrix
Report Number
DRDC-RDDC-2014-P98 — External Literature
Date of publication
02 May 2014
Number of Pages
8
DSTKIM No
CA039901
CANDIS No
800799
Format(s):
Electronic Document(PDF)

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