Image Segmentations for Through-the-Wall Radar Target Detection

Detection of stationary targets using pixel-wise likelihood ratio test (LRT) detectors has been recently proposed for through-the-wall radar imaging (TWRI) applications. We employ image segmentation techniques, in lieu of LRT, for target detection in TWRI. More specifically the widely used between-class variance (BCV) thresholding, maximum entropy segmentation, and K-means clustering are considered to aid in removing the clutter, resulting in enhanced radar images with target regions only. For the case when multiple images of the same scene are available through diversity in polarization and/or vantage points around a building structure, we propose to use image fusion, following the image segmentation step, to generate an enhanced composite image. In particular, additive, multiplicative, and fuzzy logic fusion techniques are considered. The performance of the segmentation and fusion schemes is evaluated and compared with that of the assumed LRT detector using both electromagnetic (EM) modeling and real data collected in a laboratory environment. The results show that, although the principles of segmentation and detection are different, the segmentation techniques provide either comparable or improved performance over the LRT detector. Specifically, in the cases considered, the maximum entropy segmentation produces the best results for detection of targets inside building structures. For fusion of multiple segmented images of the same scene, the fuzzy logic fusion outperforms the other methods.