Rotation of targets pose great a challenge for the design of an automatic image-based target detection system. In this paper, we propose a target detection algorithm that is robust to rotation of targets. Our key idea is to use rotation invariant features as the input for the classifier. For an image in Radon transform space, namely R(b,θ), taking the magnitude of 1-D Fourier transform on θ, we get |F_θ{R(b,θ)}|. It was proved that the coefficients of the combined Radon and 1-D Fourier transform, |F_θ{R(b,θ)}| is invariant to rotation of the image. These coefficients are used as the input to a maximum-margin classifier based on I-RELIEF feature weighting technique. Its objective is to maximize the margin between two classes and improve the robustness of the classifier against uncertainties. For each pixel of a sample SAR image, a feature vector can be extracted from a sub image centered at that pixel. Then our classifier decides whether the pixel is target or non-target. This produces a binary-valued image. We further improve the detection performance by connectivity analysis, image differencing and diversity combining. We evaluate the performance of our proposed algorithm, using the data set collected by Swedish CARABAS-II systems, and the experimental results show that our proposed algorithm achieves superior performance over the benchmark algorithm.

Date Published: 15 April 2008
PDF: 11 pages
Proc. SPIE 6970, Algorithms for Synthetic Aperture Radar Imagery XV, 69700W (15 April 2008); doi: 10.1117/12.777305

Published in SPIE Proceedings Vol. 6970:
Algorithms for Synthetic Aperture Radar Imagery XV
Edmund G. Zelnio; Frederick D. Garber, Editor(s)