TY  -  
T1  - Tree species classification from airborne hyperspectral and LiDAR data using 3D convolutional neural networks 
SN  -  /  
UR  - http://hdl.handle.net/10138/328801 
T3  -  
A1  - Mäyrä, Janne; Keski-Saari, Sarita; Kivinen, Sonja; Tanhuanpää, Topi; Hurskainen, Pekka; Kullberg, Peter; Poikolainen, Laura; Viinikka, Arto; Tuominen, Sakari; Kumpula, Timo; Vihervaara, Petteri 
A2  -  
PB  -  
Y1  - 2021 
LA  - eng 
AB  - During the last two decades, forest monitoring and inventory systems have moved from field surveys to remote sensing-based methods. These methods tend to focus on economically significant components of forests, thus leaving out many factors vital for forest biodiversity, such as the occurrence of species with low economical but high ecological values. Airborne hyperspectral imagery has shown significant potential for tree species classification, but the most common analysis methods, such as rand... 
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KW  - 1171 Geosciences; hyperspectral imaging; TREE SPECIES CLASSIFICATION; Hyperspectral imaging; tree species classification; 113 Computer and information sciences; Deep Learning; Convolutional neural network; Deep Learning; Convolutional neural network; Hyperspectral imaging; Deep learning; Convolutional neural network; Tree species classification 
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ER  -