Tree Species Recognition in Species Rich Area Using UAV-Borne Hyperspectral Imagery and Stereo-Photogrammetric Point Cloud
Tuominen, S., Näsi, R., Honkavaara, E., Balazs, A., Hakala, T., Viljanen, N., Pölönen, I., Saari, H., & Reinikainen, J. (2017). Tree Species Recognition in Species Rich Area Using UAV-Borne Hyperspectral Imagery and Stereo-Photogrammetric Point Cloud. In E. Honkavaara, B. Hu, K. Karantzalos, X. Liang, R. Müller, E. Nocerino, I. Pölönen, & P. Rönnholm (Eds.), ISPRS SPEC3D 2017 : Frontiers in Spectral imaging and 3D Technologies for Geospatial Solutions (pp. 185-194). International Society for Photogrammetry and Remote Sensing. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W3. https://doi.org/10.5194/isprs-archives-XLII-3-W3-185-2017
Julkaistu sarjassa
International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesTekijät
Näsi, R. |
Toimittajat
Hu, B. |
Päivämäärä
2017Tekijänoikeudet
© Authors 2017. This is an open access article distributed under the terms of the Creative Commons License.
Recognition of tree species and geospatial information of tree species composition is essential for forest management. In this study
we test tree species recognition using hyperspectral imagery from VNIR and SWIR camera sensors in combination with 3D
photogrammetric canopy surface model based on RGB camera stereo-imagery. An arboretum forest with a high number of tree
species was used as a test area. The imagery was acquired from the test area using UAV-borne cameras. Hyperspectral imagery
was calibrated for providing a radiometrically corrected reflectance mosaic, which was tested along with the original uncalibrated
imagery. Alternative estimators were tested for predicting tree species and genus, as well as for selecting an optimal set of remote
sensing features for this task. All tested estimators gave similar trend in the results: the calibrated reflectance values performed
better in predicting tree species and genus compared to uncorrected hyperspectral pixel values. Furthermore, the combination of
VNIR, SWIR and 3D features performed better than any of the data sets individually, with calibrated reflectances and original
pixel values alike. The highest proportion of correctly classified trees was achieved using calibrated reflectance features from
VNIR and SWIR imagery together with 3D point cloud features: 0.823 for tree species and 0.869 for tree genus.
...
Julkaisija
International Society for Photogrammetry and Remote SensingKonferenssi
Congress of the International Society for Photogrammetry and Remote SensingKuuluu julkaisuun
ISPRS SPEC3D 2017 : Frontiers in Spectral imaging and 3D Technologies for Geospatial SolutionsISSN Hae Julkaisufoorumista
1682-1750Asiasanat
Alkuperäislähde
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W3/185/2017/isprs-archives-XLII-3-W3-185-2017.pdfJulkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/27317567
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