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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >An Unsupervised Scattering Mechanism Classification Method for PolSAR Images
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An Unsupervised Scattering Mechanism Classification Method for PolSAR Images

机译：PolSAR图像的无监督散射机制分类方法

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摘要

This letter concentrates on scattering mechanism classification of polarimetric synthetic aperture radar (PolSAR) images. Scattering mechanism classes are defined as the combinations of dominant and secondary scattering mechanisms. With three metrics extracted from the observed coherency matrix, an unsupervised classifier is proposed to classify PolSAR pixels into eight combinations of surface scattering, double-bounce scattering, and volume scattering. When applying the proposed method to simulated data, the Kappa coefficient is 0.891. It effectively classifies the dominant mechanism, and the Kappa coefficient is 0.127 higher than that of the H/ $alpha$ method. Experiment using uninhabited aerial vehicle SAR data shows that the proposed method is able to identify secondary mechanism in forests and urban areas. This method is not only a good classifier free of specific polarimetric decomposition but also can serve as a preclassification step of sophisticated classification scheme.
机译：这封信集中在极化合成孔径雷达（PolSAR）图像的散射机制分类上。散射机制类别定义为主要和次要散射机制的组合。通过从观测到的相干矩阵中提取三个指标，提出了一种无监督分类器，将PolSAR像素分类为表面散射，双反弹散射和体积散射的八种组合。将拟议的方法应用于模拟数据时，卡伯系数为0.891。它有效地对主导机制进行了分类，并且Kappa系数比H / $ alpha $方法的高0.127。使用无人飞行器SAR数据进行的实验表明，该方法能够识别森林和城市地区的次要机制。该方法不仅是没有特定极化分解的良好分类器，而且可以作为复杂分类方案的预分类步骤。

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