In this paper, we examine the use of image segmentation
approaches for target detection in TWRI. The betweenclass
variance thresholding, entropy-based segmentation, and Kmeans
clustering are applied to segment target and clutter regions.
Real 2D polarimetric images are used to demonstrate that
simple histogram-based segmentation methods produce either
comparable or improved performance over the Likelihood Ratio
Tests (LRT) detector. Specifically, the results show that, for the
cases considered, the entropy-based segmentation outperforms
the other image segmentation methods and the LRT detector.