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首页> 外文期刊>International journal of image and data fusion >Texture modelling for land cover classification of fully polarimetric SAR images
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Texture modelling for land cover classification of fully polarimetric SAR images

机译：全极化SAR图像土地覆盖分类的纹理建模

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Classification based on polarimetric data alone does not provide sufficient sensitivity for classes' separation such as forests. The use of other characteristics such as texture provides a powerful tool to get better class discrimination. Thus, this article outlines an approach for texture modelling adapted to land identification using polarimetric synthetic aperture radar (PolSAR) images. As radar images may contain a myriad of textures and to take into account their non-stationarity feature, the model is non-parametric and is based on a likeness measure between neighbourhoods. This measure is incorporated in probability formulation of the textural model. The latter is validated through texture synthesis applied to Brodatz' natural textures. While having multiple applications, we focused here on the issue of terrain mapping of PolSAR images. Using Bayesian approach and some basic assumptions, we defined a classification scheme based on the textural model. It was tested on original Pauli decomposed images and filtered ones. The test area used is the Oberpfaffenhofen in Munich and the PolSAR images were acquired in the P band. For evaluation purposes, the classifications results obtained were compared with the Wishart classifier result performed on H-α partition showing interesting results and reaching classification rates ≈90%. Such an analysis has allowed us to assess the importance of texture considering and proved that the proposed texture model is able to describe the target's physical properties of PolSAR data.
机译：仅基于极化数据的分类不能为分类分离（例如森林）提供足够的敏感性。使用其他特性（例如纹理）可提供强大的工具，以更好地区分类别。因此，本文概述了一种适用于使用极化合成孔径雷达（PolSAR）图像进行土地识别的纹理建模方法。由于雷达图像可能包含大量纹理，并考虑到它们的非平稳性特征，因此该模型是非参数的，并且基于邻居之间的相似度度量。该度量被合并到纹理模型的概率公式中。后者通过应用于Brodatz自然纹理的纹理合成得到验证。在具有多种应用的同时，我们将重点放在PolSAR图像的地形映射问题上。使用贝叶斯方法和一些基本假设，我们基于纹理模型定义了分类方案。在原始的Pauli分解图像和过滤后的图像上进行了测试。使用的测试区域是慕尼黑的Oberpfaffenhofen，并且在P波段中采集了PolSAR图像。出于评估目的，将获得的分类结果与在H-α分区上执行的Wishart分类器结果进行比较，显示出有趣的结果并达到≈90％的分类率。这种分析使我们能够评估考虑纹理的重要性，并证明所提出的纹理模型能够描述目标的PolSAR数据的物理特性。

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