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Remote Sens. 2017, 9(3), 185;

Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

Finnish Geospatial Research Insititute, National Land Survey of Finland, Geodeetinrinne 2, 02430 Masala, Finland
Natural Resources Institute Finland, PL 2 00791 Helsinki, Finland
VTT Microelectronics, P.O. Box 1000, FI-02044 VTT, Finland
Department of Mathematical Information Tech., University of Jyväskylä, P.O. Box 35, FI-40014 Jyväskylä, Finland
Department of Cartography, Univ. Estadual Paulista (UNESP), Presidente Prudente, SP 19060-900, Brazil
Author to whom correspondence should be addressed.
Academic Editors: Farid Melgani, Francesco Nex, Norman Kerle and Prasad S. Thenkabail
Received: 8 December 2016 / Revised: 16 February 2017 / Accepted: 18 February 2017 / Published: 23 February 2017
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
Full-Text   |   PDF [11211 KB, uploaded 23 February 2017]   |  


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. View Full-Text
Keywords: UAV; hyperspectral; photogrammetry; radiometry; point cloud; forest; classification UAV; hyperspectral; photogrammetry; radiometry; point cloud; forest; classification

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Nevalainen, O.; Honkavaara, E.; Tuominen, S.; Viljanen, N.; Hakala, T.; Yu, X.; Hyyppä, J.; Saari, H.; Pölönen, I.; Imai, N.N.; Tommaselli, A.M.G. Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging. Remote Sens. 2017, 9, 185.

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