A new statistical model for Markovian classification of urban areas in high-resolution SAR images
A new statistical model for Markovian classification of urban areas in high-resolution SAR images
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DOI：
10.1109/tgrs.2004.834630
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发表时间：
2004-10
影响因子：
8.2
通讯作者：
C. Tison;J. Nicolas;F. Tupin;H. Maître
中科院分区：
文献类型：
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作者：
C. Tison;J. Nicolas;F. Tupin;H. Maître
提出了一种适用于城市地区高分辨率合成孔径雷达（SAR）图像的分类方法。在处理SAR图像时，强烈需要散射的统计模型来考虑乘性噪声和高动态。例如，分类过程需要以使用统计数据为基础。我们的主要贡献是选择一个准确的模型，高分辨率的SAR图像在城市地区和马尔可夫分类算法中的使用。SAR图像中的杂波在分辨率较高或人为干扰的情况下会呈现非高斯分布。已经提出了许多模型来拟合非高斯散射统计（K、Weibull、对数正态、Nakagami-Rice等），但没有一个足够灵活，可以在我们的环境中模拟所有类型的表面。因此，我们使用一个数学模型，依赖于Fisher分布和对数矩估计，这是相关的一个看数据。这种估计方法是基于第二类统计量，这是详细的文件。我们还证明了其精度为城市地区在高分辨率。通过混合该模型和马尔可夫分割获得的分类质量很高，使我们能够区分地面，建筑物和植被。
We propose a classification method suitable for high-resolution synthetic aperture radar (SAR) images over urban areas. When processing SAR images, there is a strong need for statistical models of scattering to take into account multiplicative noise and high dynamics. For instance, the classification process needs to be based on the use of statistics. Our main contribution is the choice of an accurate model for high-resolution SAR images over urban areas and its use in a Markovian classification algorithm. Clutter in SAR images becomes non-Gaussian when the resolution is high or when the area is man-made. Many models have been proposed to fit with non-Gaussian scattering statistics (K, Weibull, Log-normal, Nakagami-Rice, etc.), but none of them is flexible enough to model all kinds of surfaces in our context. As a consequence, we use a mathematical model that relies on the Fisher distribution and the log-moment estimation and which is relevant for one-look data. This estimation method is based on the second-kind statistics, which are detailed in the paper. We also prove its accuracy for urban areas at high resolution. The quality of the classification that is obtained by mixing this model and a Markovian segmentation is high and enables us to distinguish between ground, buildings, and vegetation.