Minimum description length synthetic aperture radar image segmentation
Minimum description length synthetic aperture radar image segmentation
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DOI：
10.1109/tip.2003.816005
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发表时间：
2003-09
期刊：
影响因子：
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通讯作者：
F. Galland;N. Bertaux;P. Réfrégier
中科院分区：
文献类型：
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作者：
F. Galland;N. Bertaux;P. Réfrégier
我们提出了一种新的最小描述长度（MDL）的方法的基础上的变形分区-多边形网格-由几个均匀的区域组成的斑点图像的自动分割。因此，图像分割包括对多边形网格的估计，或者更准确地说，其区域的数量、其节点的数量和其节点的位置。这些估计是通过最小化一个独特的MDL标准，考虑到散斑波动的概率特性和多边形网格的随机复杂性的措施。这种方法，然后导致一个全球MDL标准没有一个待定的参数，因为没有其他正则化项比随机复杂的多边形网格是必要的，噪声参数可以估计与最大似然方法。通过对合成和真实的农区合成孔径雷达图像的分析，验证了该方法的有效性，并分析了模型中各参数的影响。
We present a new minimum description length (MDL) approach based on a deformable partition--a polygonal grid--for automatic segmentation of a speckled image composed of several homogeneous regions. The image segmentation thus consists in the estimation of the polygonal grid, or, more precisely, its number of regions, its number of nodes and the location of its nodes. These estimations are performed by minimizing a unique MDL criterion which takes into account the probabilistic properties of speckle fluctuations and a measure of the stochastic complexity of the polygonal grid. This approach then leads to a global MDL criterion without an undetermined parameter since no other regularization term than the stochastic complexity of the polygonal grid is necessary and noise parameters can be estimated with maximum likelihood-like approaches. The performance of this technique is illustrated on synthetic and real synthetic aperture radar images of agricultural regions and the influence of different terms of the model is analyzed.