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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Level-Set Formulation Based on an Infinite Series of Sample Moments for SAR Image Segmentation
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Level-Set Formulation Based on an Infinite Series of Sample Moments for SAR Image Segmentation

机译:基于无限系列的SAR图像分割样本矩的级别配方

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摘要

SAR image segmentation plays a central role in geoscience and remote sensing of the environment. Recently, methodologies that apply traditional segmentation algorithms to maps of statistical information extracted from SAR image rather than to the raw data itself have shown promising results. Nonetheless, the application of more powerful segmentation methods to these maps is constrained by the lack of adequate statistical models for such data. In this letter, we present a level-set-based algorithm that embodies much of the data statistics without assuming any prior model for it. We also evaluated its performance on both real and synthetic SAR images.
机译:SAR图像分割在地球科学和环境的遥感中起着核心作用。最近,将传统分割算法应用于从SAR图像提取而不是原始数据本身提取的统计信息映射的方法表明了有希望的结果。尽管如此,对这些地图的更强大的分段方法的应用受到此类数据的足够统计模型的限制。在这封信中,我们介绍了一种基于级别的基于级别的算法,其体现了大部分数据统计信息而不假设它的任何先前模型。我们还在实际和合成的SAR图像上评估其性能。

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