UAS based tree species identification using the novel FPI based hyperspectral cameras in visible, NIR and SWIR spectral ranges
Näsi, R., Honkavaara, E., Tuominen, S., Saari, H., Pölönen, I., Hakala, T., Viljanen, N., Soukkamäki, J., Näkki, I., Ojanen, H., & Reinikainen, J. (2016). UAS based tree species identification using the novel FPI based hyperspectral cameras in visible, NIR and SWIR spectral ranges. In L. Halounova, V. Šaafář, C. K. Toth, J. Karas, G. Huadong, N. Haala, A. Habib, P. Reinartz, X. Tang, J. Li, C. Armenakis, G. Grenzdörffer, P. L. Roux, S. Stylianidis, R. Blasi, M. Menard, H. Dufourmount, & Z. Li (Eds.), Proceedings of the XXIII ISPRS Congress (pp. 1143-1148). International Society for Photogrammetry and Remote Sensing. International archives of the photogrammetry, remote sensing and spatial information sciences, Volume XLI-B1. https://doi.org/10.5194/isprs-archives-XLI-B1-1149-2016
Published in
International archives of the photogrammetry, remote sensing and spatial information sciencesAuthors
Näsi, R. |
Date
2016Copyright
© the Authors, 2016. This is an open access article distributed under the terms of Creative Commons License.
Unmanned airborne systems (UAS) based remote sensing offers flexible tool for environmental monitoring. Novel lightweight
Fabry-Perot interferometer (FPI) based, frame format, hyperspectral imaging in the spectral range from 400 to 1600 nm was used for
identifying different species of trees in a forest area. To the best of the authors’ knowledge, this was the first research where
stereoscopic, hyperspectral VIS, NIR, SWIR data is collected for tree species identification using UAS. The first results of the
analysis based on fusion of two FPI-based hyperspectral imagers and RGB camera showed that the novel FPI hyperspectral
technology provided accurate geometric, radiometric and spectral information in a forested scene and is operational for
environmental remote sensing applications.
Publisher
International Society for Photogrammetry and Remote SensingConference
Congress of the International Society for Photogrammetry and Remote SensingIs part of publication
Proceedings of the XXIII ISPRS CongressISSN Search the Publication Forum
1682-1750Keywords
Original source
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B1/1143/2016/isprs-archives-XLI-B1-1143-2016.pdfPublication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/26131605
Metadata
Show full item recordCollections
License
Except where otherwise noted, this item's license is described as © the Authors, 2016. This is an open access article distributed under the terms of Creative Commons License.
Related items
Showing items with similar title or keywords.
-
Remote Sensing of 3-D Geometry and Surface Moisture of a Peat Production Area Using Hyperspectral Frame Cameras in Visible to Short-Wave Infrared Spectral Ranges Onboard a Small Unmanned Airborne Vehicle (UAV)
Honkavaara, Eija; Eskelinen, Matti; Pölönen, Ilkka; Saari, Heikki; Ojanen, Harri; Mannila, Rami; Holmlund, Christer; Hakala, Teemu; Litkey, Paula; Rosnell, Tomi; Viljanen, Niko; Pulkkanen, Merja (Institute of Electrical and Electronics Engineers, 2016)Miniaturized hyperspectral imaging sensors are becoming available to small unmanned airborne vehicle (UAV) platforms. Imaging concepts based on frame format offer an attractive alternative to conventional hyperspectral ... -
Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture
Honkavaara, Eija; Saari, Heikki; Kaivosoja, Jere; Pölönen, Ilkka; Hakala, Teemu; Litkey, Paula; Mäkynen, Jussi; Pesonen, Liisa (MDPI AG, 2013)Imaging using lightweight, unmanned airborne vehicles (UAVs) is one of the most rapidly developing fields in remote sensing technology. The new, tunable, Fabry-Perot interferometer-based (FPI) spectral camera, which weighs ... -
Tree Species Identification Using 3D Spectral Data and 3D Convolutional Neural Network
Pölönen, Ilkka; Annala, Leevi; Rahkonen, Samuli; Nevalainen, Olli; Honkavaara, Eija; Tuominen, Sakari; Viljanen, Niko; Hakala, Teemu (IEEE, 2019)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, ... -
Autonomous hyperspectral UAS photogrammetry for environmental monitoring applications
Honkavaara, E.; Hakala, T.; Markelin, L.; Jaakkola, A.; Saari, H.; Ojanen, H.; Pölönen, Ilkka; Tuominen, S.; Näsi, R.; Rosnell, T.; Viljanen, N. (International Society for Photogrammetry and Remote Sensing (ISPRS), 2014)The unmanned airborne system (UAS) remote sensing using lightweight multi- and hyperspectral imaging sensors offer new possibilities for the environmental monitoring applications. Based on the accurate measurements of the ... -
Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging
Nevalainen, Olli; Honkavaara, Eija; Tuominen, Sakari; Viljanen, Niko; Hakala, Teemu; Yu, Xiaowei; Hyyppä, Juha; Saari, Heikki; Pölönen, Ilkka; Imai, Nilton N.; Tommaselli, Antonio M. G. (MPDI, 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. ...