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首页> 外文期刊>European Journal of Remote Sensing >Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery
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Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery

机译：使用超高分辨率无人机系统图像对湿地植被进行基于对象的分类

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ABSTRACT The purpose of this study is to examine the use of multi-resolution object-based classification methods for the classification of Unmanned Aircraft Systems (UAS) images of wetland vegetation and to compare its performance with pixel-based classification approaches. Three types of classifiers (Support Vector Machine, Artificial Neural Network and Maximum Likelihood) were utilized to classify the object-based images, the original 8-cm UAS images and the down-sampled (30????cm) version of the image. The results of the object-based and two pixel-based classifications were evaluated and compared. Object-based classification produced higher accuracy than pixel-based classifications if the same type of classifier is used. Our results also showed that under the same classification scheme (i.e. object or pixel), the Support Vector Machine classifier performed slightly better than Artificial Neural Network, which often yielded better results than Maximum Likelihood. With an overall accuracy of 70.78%, object-based classification using Support Vector Machine showed the best performance. This study also concludes that while UAS has the potential to provide flexible and feasible solutions for wetland mapping, some issues related to image quality still need to be addressed in order to improve the classification performance.
机译：摘要这项研究的目的是研究基于多分辨率对象的分类方法对湿地植被的无人机系统（UAS）图像进行分类，并将其性能与基于像素的分类方法进行比较。利用三种类型的分类器（支持向量机，人工神经网络和最大似然法）对基于对象的图像，原始的8厘米UAS图像和图像的降采样（30 ???? cm）版本进行分类。 。对基于对象和基于两个像素的分类结果进行了评估和比较。如果使用相同类型的分类器，则基于对象的分类将比基于像素的分类产生更高的准确性。我们的结果还表明，在相同的分类方案（即对象或像素）下，支持向量机分类器的性能略优于人工神经网络，后者通常比最大似然性产生更好的结果。使用支持向量机的基于对象的分类的总体准确性为70.78％，显示了最佳性能。这项研究还得出结论，尽管UAS有潜力为湿地制图提供灵活可行的解决方案，但仍需要解决一些与图像质量有关的问题，以提高分类性能。

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