Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging
Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging
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DOI：
10.3390/rs9030185
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发表时间：
2017-03-01
期刊：
影响因子：
5
通讯作者：
Tommaselli, Antonio M. G.
中科院分区：
文献类型：
--
作者：
Nevalainen, Olli;Honkavaara, Eija;Tommaselli, Antonio M. G.
基于小型无人机（UAV）的遥感是一项快速发展的技术。新的传感器和方法正在进入市场，为执行遥感任务提供了全新的可能性。三维（3D）高光谱遥感是一种新的和强大的技术，最近已成为小型无人机。本文研究了基于无人机的摄影测量和高光谱成像在北方森林中的单木检测和树种分类中的性能。2014年6月，使用配备帧格式高光谱相机和RGB相机的无人机遥感系统在高度多变的天气条件下收集了11个测试点的4151棵代表不同树种和发育阶段的参考树。密集的点云测量摄影测量自动图像匹配使用高分辨率RGB图像与5厘米的点间距。光谱特征从高光谱图像块中获得，其大的辐射变化通过使用基于辐射块平差的新方法在飞行中辐照度观测的支持下进行补偿。光谱和3D点云特征用于各种分类器的分类实验。随机森林和多层感知器（MLP）获得了最好的结果，两者都给出了95%的总体准确率和0.93的F分数。根据该地区的特点，从摄影测量点云识别单个树木的准确性在40%和95%之间变化。参考测量方面的挑战也可能减少了这些数字。结果令人鼓舞，表明即使在非常困难的条件下，高光谱三维遥感也可以从无人机平台上运行。这些新方法有望在不久的将来为各种环境近距离遥感任务的自动化提供一个强大的工具。
Small unmanned aerial vehicle (UAV) based remote sensing is a rapidly evolving technology. Novel sensors and methods are entering the market, offering completely new possibilities to carry out remote sensing tasks. Three-dimensional (3D) hyperspectral remote sensing is a novel and powerful technology that has recently become available to small UAVs. This study investigated the performance of UAV-based photogrammetry and hyperspectral imaging in individual tree detection and tree species classification in boreal forests. Eleven test sites with 4151 reference trees representing various tree species and developmental stages were collected in June 2014 using a UAV remote sensing system equipped with a frame format hyperspectral camera and an RGB camera in highly variable weather conditions. Dense point clouds were measured photogrammetrically by automatic image matching using high resolution RGB images with a 5 cm point interval. Spectral features were obtained from the hyperspectral image blocks, the large radiometric variation of which was compensated for by using a novel approach based on radiometric block adjustment with the support of in-flight irradiance observations. Spectral and 3D point cloud features were used in the classification experiment with various classifiers. The best results were obtained with Random Forest and Multilayer Perceptron (MLP) which both gave 95% overall accuracies and an F-score of 0.93. Accuracy of individual tree identification from the photogrammetric point clouds varied between 40% and 95%, depending on the characteristics of the area. Challenges in reference measurements might also have reduced these numbers. Results were promising, indicating that hyperspectral 3D remote sensing was operational from a UAV platform even in very difficult conditions. These novel methods are expected to provide a powerful tool for automating various environmental close-range remote sensing tasks in the very near future.