Fast threshold selection algorithm for segmentation of synthetic aperture radar images
Fast threshold selection algorithm for segmentation of synthetic aperture radar images
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DOI：
10.1049/iet-rsn.2011.0341
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发表时间：
2012-10
影响因子：
1.7
通讯作者：
J. J. Ranjani-J.;S. Thiruvengadam
中科院分区：
文献类型：
--
作者：
J. J. Ranjani-J.;S. Thiruvengadam
合成孔径雷达（SAR）图像中不相交区域的自动检测，需要定位和识别的对象的应用是复杂的散斑的性质。提出了一种多级指数加权平均比（MROEWA）和一种快速的最优阈值选择算法用于SAR图像分割。一个非参数和无监督的原则，使用的灰度直方图是用于产生的区域是同质的。一个最佳的阈值是自动选择的最大化的灰度级的类的可分性，通过结合一个简单的搜索策略。对合成图像和真实的图像的实验结果验证了该方法的有效性。实验结果表明，该方法通过减少计算最优阈值所需的算术运算次数，大大提高了检测性能。
Automatic detection of disjoint regions in synthetic aperture radar (SAR) images for applications requiring localisation and identification of objects is complicated by the nature of the speckle. A multi-level ratio of exponential weighted averages (MROEWA) together with a fast algorithm for optimal threshold selection is proposed for SAR segmentation. A non-parametric and unsupervised principle using the grey level histogram is utilised for producing the regions that are homogenous. An optimal threshold is automatically selected by maximising the separability of the classes in grey level by incorporating a simple search strategy. Experimental results on both synthetic and real images verify the effectiveness of the proposed method. It is validated that the proposed method outperforms greatly by reducing the number of arithmetic operations required for computing the optimal threshold.