B3 Non-refereed conference proceedings
Tree Species Identification Using 3D Spectral Data and 3D Convolutional Neural Network (2019)


Pölönen, I., Annala, L., Rahkonen, S., Nevalainen, O., Honkavaara, E., Tuominen, S., Viljanen, N., & Hakala, T. (2019). Tree Species Identification Using 3D Spectral Data and 3D Convolutional Neural Network. In WHISPERS 2018 : 9th Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing. IEEE. https://doi.org/10.1109/WHISPERS.2018.8747253


JYU authors or editors


Publication details

All authors or editors: Pölönen, Ilkka; Annala, Leevi; Rahkonen, Samuli; Nevalainen, Olli; Honkavaara, Eija; Tuominen, Sakari; Viljanen, Niko; Hakala, Teemu

Parent publication: WHISPERS 2018 : 9th Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing

ISBN: 978-1-7281-1581-8

ISSN: 2158-6276

Publication year: 2019

Publisher: IEEE

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/WHISPERS.2018.8747253

Publication open access: Not open

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/73412

Additional information: 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). 23-26 Sept. 2018, Amsterdam, Netherlands.


Abstract

In this study we apply 3D convolutional neural network (CNN) for tree species identification. Study includes the three
most common Finnish tree species. Study uses a relatively large high-resolution spectral data set, which contains also a digital surface model for the trees. Data has been gathered using an unmanned aerial vehicle, a framing hyperspectral imager and a regular RGB camera. Achieved classification results are promising by with overall accuracy of 96.2 % for the classification of the validation data set.


Keywords: tree species; spectral imaging; Three-dimensional imaging; automated pattern recognition; neural networks (information technology)

Free keywords: 3D; convolutional neural network; UAV


Contributing organizations


Related projects


Ministry reporting: Yes

Reporting Year: 2019


Last updated on 2021-20-09 at 15:46