INDIVIDUAL TREE SPECIES CLASSIFICATION BASED ON TERRESTRIAL LASER SCANNING USING CURVATURE ESTIMATION AND CONVOLUTIONAL NEURAL NETWORK
INDIVIDUAL TREE SPECIES CLASSIFICATION BASED ON TERRESTRIAL LASER SCANNING USING CURVATURE ESTIMATION AND CONVOLUTIONAL NEURAL NETWORK
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DOI：
10.5194/isprs-archives-xlii-2-w13-1077-2019
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
T. Mizoguchi;A. Ishii;H. Nakamura
中科院分区：
文献类型：
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作者：
T. Mizoguchi;A. Ishii;H. Nakamura
摘要。本文提出了一种基于地面激光扫描仪和卷积神经网络（CNN）捕获的点云深度和曲率图像创建的新方法来指定单个树种。给定一棵树的点云，该方法首先提取胸高处树干对应的点子集。然后通过基于RANSAC的圆拟合去除提取点上的树枝和叶子，并通过对剩余树干点进行三次多项式曲面的全局拟合来生成深度图像。此外，通过局部拟合邻近点的二次曲面来估计每个扫描点的主曲率。深度图像清晰地捕捉到树皮劈裂和撕裂所涉及的纹理，但其计算不稳定，可能无法在所得图像中获取树皮形状。曲率估计可以稳定地计算表面凹凸度，可以很好地表示曲率图像中树皮纹理的局部几何形状。与深度图像相比，曲率图像可以对有许多枝叶的倾斜树木进行准确的分类。我们还评估了一种多模态物种分类方法的有效性，该方法使用CNN和支持向量机一起分析深度和曲率图像。通过对杉木和柏木点云的分析，验证了该方法的优越性。
Abstract. In this paper, we propose a new method for specifying individual tree species based on depth and curvature image creation from point cloud captured by terrestrial laser scanner and Convolutional Neural Network (CNN). Given a point cloud of an individual tree, the proposed method first extracts the subset of points corresponding to a trunk at breast-height. Then branches and leaves are removed from the extracted points by RANSAC -based circle fitting, and the depth image is created by globally fitting a cubic polynomial surface to the remaining trunk points. Furthermore, principal curvatures are estimated at each scanned point by locally fitting a quadratic surface to its neighbouring points. Depth images clearly capture the bark texture involved by its split and tear-off, but its computation is unstable and may fail to acquire bark shape in the resulting images. In contrast, curvature estimation enables stable computation of surface concavity and convexity, and thus it can well represent local geometry of bark texture in the curvature images. In comparison to the depth image, the curvature image enables accurate classification for slanted trees with many branches and leaves. We also evaluated the effectiveness of a multi-modal approach for species classification in which depth and curvature images are analysed together using CNN and support vector machine. We verified the superior performance of our proposed method for point cloud of Japanese cedar and cypress trees.