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首页> 外文期刊>Journal of Applied Remote Sensing >Analyzing the role of spatial features when cooperating hyperspectral and LiDAR data for the tree species classification in a subtropical plantation forest area
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Analyzing the role of spatial features when cooperating hyperspectral and LiDAR data for the tree species classification in a subtropical plantation forest area

机译：分析空间特征在亚热带种植林地区树种类分类的高光谱和激光雷达数据时的作用

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摘要

Forest has complex horizontal and vertical structures, so it is difficult to accurately identify tree species from remote sensing images only by spectral characteristics. Based on the airborne hyperspectral and LiDAR data sets, adding spatial features into tree species identifying procedure was studied in Gaofeng Forest Farm of Guangxi, China. As a result, a tree species classification method of synergizing spectral, spatial, and vertical structure features was proposed. First, principal components (PCs) were extracted from the hyperspectral data acquired by the AISA Eagle II sensor as spectral features. Four typical spatial features, gray level co-occurrence matrix, extended morphological profile, extended multiattribute profile, and multiscale guided filtering (MGF), were derived from the principal component bands. Meanwhile, digital elevation model, digital surface model (DSM), and normalized DSM were calculated from the LiDAR data to represent the characteristics of vertical structure. Then 13 spectral-spatial-vertical feature combinations were built and compared to classify the tree species using random forest classifier. The experimental results show that both spatial and vertical structure features can effectively improve the classification accuracy of tree species when banding with spectral features, and the feature combinations including multiscale spatial features is more conducive to improve classification accuracy. Compared with the method of using spectral features only, the methods with spatial features or vertical structure features could enhance the overall accuracy (OA) maximally by 49.49% and 14.65%. Among all the feature combinations, the synergy of PC bands, DSM, and MGF features achieves the highest classification accuracy, with OA 96.10% and kappa 0.9575. The spatial features have a great application potential for tree species classification in a plantation forest environment. (C) 2020 Society of Photo Optical Instrumentation Engineers (SPIE)
机译：森林具有复杂的水平和垂直结构，因此难以通过光谱特性从遥感图像中准确地识别树种。基于空中高光谱和激光雷达数据集，在中国广西高峰森林农场研究了树种识别程序中的空间特征。结果，提出了一种协同频率，空间和垂直结构特征的树种分类方法。首先，从AISA Eagle II传感器获取的高光谱数据中提取主成分（PC）作为光谱特征。四个典型的空间特征，灰度级共发生矩阵，延长形态剖面，延长的多特征概况和多尺寸引导滤波（MGF）源自主组件频带。同时，从LIDAR数据计算数字高度模型，数字表面模型（DSM）和归一化DSM，以表示垂直结构的特性。然后建立13个光谱 - 空间 - 垂直特征组合，并使用随机林分类器对树种进行分类。实验结果表明，当使用光谱特征时，空间和垂直结构特征可以有效地提高树种的分类精度，并且包括多尺度空间特征的特征组合更有利于提高分类精度。与仅使用光谱特征的方法相比，空间特征或垂直结构特征的方法可以提高整体精度（OA）最大值49.49％和14.65％。在所有特征组合中，PC频带，DSM和MGF特征的协同作用达到了最高分类精度，OA 96.10％和Kappa 0.9575。空间特征在种植林环境中具有良好的树种分类应用潜力。 （c）2020年照片光学仪表工程师（SPIE）

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