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首页> 外文期刊>Ecological informatics: an international journal on ecoinformatics and computational ecology >Feature-location analyses for identification of urban tree species from very high resolution remote sensing data
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Feature-location analyses for identification of urban tree species from very high resolution remote sensing data

机译：从高分辨率遥感数据中识别城市树木种类的特征定位分析

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The interference from background noises and the weak spectral separability between species have negative impacts on the identification of urban tree species from remote sensing images. The density of neighbouring members (members mean both pixels and patches) similar to the centre pixel in some image features may offer an opportunity to improve the separability. This paper focuses on the density-involved feature-location analyses (refer to as F-L analyses) developed from the framework of integrated analysis of feature and space. We expressed the density of feature-carried members in two elaborated models: using the density dimension and adding the density descriptors to a feature space to conduct the F-L analyses during a procedure of classifying urban tree species. Experimental results indicate that the two models cannot only increase the number of available independent components for constructing an input vector therefore making the feature space richer, but also provide the reference of spatial dependence among the feature-carried members thus finally making the identification less difficult. The method with the density-involved F-L analyses obviously outperforms that with only conventional spectral features analyses in the classification. The average overall accuracy (OA) derived from the former is 23% higher than that from the latter. (C) 2015 Elsevier B.V. All rights reserved.
机译：背景噪声的干扰以及物种之间的弱光谱可分离性对从遥感图像识别城市树木物种产生负面影响。在某些图像特征中，类似于中心像素的相邻成员的密度（成员既代表像素又代表像素）可以为改善可分离性提供机会。本文重点研究从特征和空间集成分析框架开发的涉及密度的特征位置分析（称为F-L分析）。我们在两个详细模型中表达了特征承载成员的密度：使用密度维并将密度描述符添加到特征空间，以在对城市树种进行分类的过程中进行F-L分析。实验结果表明，这两种模型不仅增加了用于构建输入向量的可用独立分量的数量，从而使特征空间更丰富，而且为特征承载成员之间的空间依赖性提供了参考，从而最终使识别变得简单。涉及密度的F-L分析的方法明显优于仅采用常规光谱特征分析的方法。前者的平均总体准确度（OA）比后者高23％。 （C）2015 Elsevier B.V.保留所有权利。

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