Center for Automation Research, Department of Electrical Engineering, University of Maryland, College Park, MD 20742-3275, USA.
IEEE Trans Image Process. 1999;8(12):1823-31. doi: 10.1109/83.806628.
Detecting targets occluded by foliage in foliage-penetrating (FOPEN) ultra-wideband synthetic aperture radar (UWB SAR) images is an important and challenging problem. Given the different nature of target returns in foliage and nonfoliage regions and very low signal-to-clutter ratio in UWB imagery, conventional detection algorithms fail to yield robust target detection results. A new target detection algorithm is proposed that (1) incorporates symmetric alpha-stable (SalphaS) distributions for accurate clutter modeling, (2) constructs a two-dimensional (2-D) site model for deriving local context, and (3) exploits the site model for region-adaptive target detection. Theoretical and empirical evidence is given to support the use of the SalphaS model for image segmentation and constant false alarm rate (CFAR) detection. Results of our algorithm on real FOPEN images collected by the Army Research Laboratory are provided.
检测植被穿透 (FOPEN) 超宽带合成孔径雷达 (UWB SAR) 图像中被植被遮挡的目标是一个重要且具有挑战性的问题。鉴于植被和非植被区域目标回波的性质不同，以及 UWB 图像中非常低的信噪比，传统的检测算法无法得到稳健的目标检测结果。提出了一种新的目标检测算法，该算法 (1) 采用对称 α-稳定 (SalphaS) 分布进行精确的杂波建模，(2) 构建二维 (2-D) 站点模型以推导出局部上下文，以及 (3) 利用站点模型进行区域自适应目标检测。给出了理论和经验证据来支持使用 SalphaS 模型进行图像分割和恒虚警率 (CFAR) 检测。提供了我们的算法在陆军研究实验室采集的真实 FOPEN 图像上的结果。
