Roadside trees are a vital component of urban greenery and play an important role in intelligent transportation and environmental protection. Quickly and efficiently identifying the spatial distribution of roadside trees is key to providing basic data for urban management and conservation decisions. In this study, we researched the potential of data fusing the Gaofen-2 (GF-2) satellite imagery rich in spectral information and mobile light detection and ranging (lidar) system (MLS) high-precision three-dimensional data to improve roadside tree classification accuracy. Specifically, a normalized digital surface model (nDSM) was derived from the lidar point cloud. GF-2 imagery was fused with an nDSM at the pixel level using the Gram–Schmidt algorithm. Then, samples were set including roadside tree samples from lidar data extracted by random sample consensus and other objects samples from field observation using the Global Positioning System. Finally, we conducted a segmentation pro...

中文翻译：

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使用移动激光雷达数据和高分2号图像对路边树木进行分类

路边树木是城市绿化的重要组成部分，在智能交通和环境保护中起着重要作用。快速有效地识别路边树木的空间分布，是为城市管理和保护决策提供基础数据的关键。在这项研究中，我们研究了将具有丰富光谱信息的高分2号（GF-2）卫星图像与移动光检测和测距（激光）系统（MLS）高精度三维数据融合以改善路边树木的数据潜力分类准确性。具体来说，归一化数字表面模型（nDSM）是从激光雷达点云导出的。使用Gram–Schmidt算法将GF-2图像与nDSM在像素级别融合。然后，设置样本，包括通过随机样本共识从激光雷达数据中提取的路边树样本以及使用全球定位系统从野外观测中获取的其他物体样本。最后，我们进行了细分过程...