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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Tree Detection in Urban Regions Using Aerial Lidar and Image Data
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Tree Detection in Urban Regions Using Aerial Lidar and Image Data

机译：利用航空激光雷达和图像数据检测城市地区的树木

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摘要

In this letter, we present an approach to detecting trees in registered aerial image and range data obtained via lidar. The motivation for this problem comes from automated 3-D city modeling, in which such data are used to generate the models. Representing the trees in these models is problematic because the data are usually too sparsely sampled in tree regions to create an accurate 3-D model of the trees. Furthermore, including the tree data points interferes with the polygonization step of the building roof top models. Therefore, it is advantageous to detect and remove points that represent trees in both lidar and aerial imagery. In this letter, we propose a two-step method for tree detection consisting of segmentation followed by classification. The segmentation is done using a simple region-growing algorithm using weighted features from aerial image and lidar, such as height, texture map, height variation, and normal vector estimates. The weights for the features are determined using a learning method on random walks. The classification is done using the weighted support vector machines, allowing us to control the misclassification rate. The overall problem is formulated as a binary detection problem, and the results presented as receiver operating characteristic curves are shown to validate our approach.
机译：在这封信中，我们提出了一种在通过激光雷达获得的注册航拍图像和距离数据中检测树木的方法。该问题的动机来自于自动3-D城市建模，其中使用了此类数据来生成模型。在这些模型中表示树木是有问题的，因为通常在树木区域中稀疏地采样数据以致无法创建树木的精确3D模型。此外，包含树数据点会干扰建筑物屋顶模型的多边形化步骤。因此，在激光雷达和航拍图像中检测并去除代表树木的点是有利的。在这封信中，我们提出了一种用于树检测的两步方法，该方法包括分段和分类。使用简单的区域增长算法进行分割，该算法使用了航空图像和激光雷达的加权特征，例如高度，纹理贴图，高度变化和法向矢量估计。使用随机游走的学习方法确定特征的权重。使用加权支持向量机进行分类，使我们能够控制错误分类率。整个问题被表述为二进制检测问题，并显示为接收器工作特性曲线的结果证明了我们的方法。

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