基于G^H模型的b样条可变形域极化SAR区域边界检测

J. Gambini, M. Mejail, J. Jacobo-Berlles, A. Frery
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引用次数: 3

摘要

本文提出了一种偏振合成孔径雷达(SAR)图像区域边界检测的新方法。它基于一种新的极化SAR数据模型,并使用b样条活动轮廓进行图像分割。为了检测区域的边界,该算法指定了一条初始b样条曲线,并使用可变形轮廓技术来寻找边界。在此过程中,估计数据的偏振G^H模型的统计参数,以便找到被分割区域与周围区域之间的过渡点。该算法可以看作是一种局部算法,它只对待分割的区域起作用,而不是对整个图像起作用
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Polarimetric SAR Region Boundary Detection Using B-Spline Deformable Countours under the G^H Model
In this paper a new approach to polarimetric Synthetic Aperture Radar (SAR) image region boundary detection is presented. It is based on a new model for polarimetric SAR data and the use of B-Spline active contours for image segmentation. In order to detect the boundary for a region, an initial B-Spline curve is specified and the proposed algorithm uses a deformable contours technique to find the boundary. In doing this, the statistical parameters of the polarimetric G^H model for the data are estimated, in order to find the transition points between the region being segmented and the surrounding area. This algorithm can be regarded as a local one, in the sense that it works on the region to be segmented instead of on the whole image
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