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首页> 外文期刊>Proceedings >Classifying UAVSAR Polarimetric Synthetic Aperture Radar (PolSAR) Imagery Using Target Decomposition Features
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Classifying UAVSAR Polarimetric Synthetic Aperture Radar (PolSAR) Imagery Using Target Decomposition Features

机译：使用目标分解特征对UAVSAR极化合成孔径雷达（PolSAR）影像进行分类

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Changes in the earth's surface significantly increase natural disasters, resulting in severe damage to man-made objects, such as roads, buildings, bridges, and so on. Radar techniques have advantages, such as lack of sensitivity to weather conditions, to night and day, and to cloud cover conditions, which can be used to identify, alert, and mitigate these damages. Because of the importance of these areas and the need to care for them, land-use classification, one of the important applications of remote sensing, is performed. Polarimetric synthetic aperture radar (PolSAR) images have many capabilities, having the scattering information on four polarized levels (HH, HV, VH and VV) and consequently depending on the shape and structure of the environment. In this study, unmaned aerial vehicle (UAVSAR) image is used. The support vector machine (SVM) model is a well-known classification method, able to run on different types of features and to distinguish classes that are not linearly separable. On the other hand, it is possible to use data mining methods to facilitate data analysis, like classifications. In this regards, it is recommended to use the random forest (RF) technique. The RF is one of the useful methods for data classification which uses a tree structure for decision-making. This method uses strategies to enhance the probability of reaching goals with conditional probability. In this study, by incorporating a variety of target decomposition methods in PolSAR images, images producing the land cover types were generated. Then, 70 features were obtained by applying the support vector machine (SVM), random forest (RF) , and K-nearest neighbor (KNN) classification methods. In order to estimate accuracy, the output of these methods was evaluated by reference data.
机译：地球表面的变化大大增加了自然灾害，严重破坏了人造物体，例如道路，建筑物，桥梁等。雷达技术具有优势，例如对天气状况，夜间和白天以及对云层状况缺乏敏感性，这些可用于识别，警告和减轻这些损害。由于这些领域的重要性和需要加以照顾，因此进行了土地利用分类，这是遥感的重要应用之一。极化合成孔径雷达（PolSAR）图像具有许多功能，具有四个极化级别（HH，HV，VH和VV）的散射信息，因此取决于环境的形状和结构。在这项研究中，使用了无人飞行器（UAVSAR）图像。支持向量机（SVM）模型是一种众所周知的分类方法，能够在不同类型的要素上运行并区分不可线性分离的类。另一方面，可以使用数据挖掘方法来促进数据分析，例如分类。在这方面，建议使用随机森林（RF）技术。 RF是用于数据分类的有用方法之一，该方法使用树结构进行决策。该方法使用策略来提高有条件概率达到目标的概率。在这项研究中，通过在PolSAR图像中结合各种目标分解方法，生成了产生土地覆盖类型的图像。然后，通过应用支持向量机（SVM），随机森林（RF）和K近邻（KNN）分类方法获得70个特征。为了估计准确性，这些方法的输出通过参考数据进行了评估。

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