Radiometric correction of multitemporal hyperspectral uas image mosaics of seedling stands

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Markelin , L , Honkavaara , E , Näsi , R , Viljanen , N , Rosnell , T , Hakala , T , Vastaranta , M , Koivisto , T & Holopainen , M 2017 , ' Radiometric correction of multitemporal hyperspectral uas image mosaics of seedling stands ' , The international archives of the photogrammetry, remote sensing and spatial information sciences , vol. 42 , no. 3/W3 , pp. 113-118 . https://doi.org/10.5194/isprs-archives-XLII-3-W3-113-2017

Title: Radiometric correction of multitemporal hyperspectral uas image mosaics of seedling stands
Author: Markelin, L.; Honkavaara, E.; Näsi, R.; Viljanen, N.; Rosnell, T.; Hakala, T.; Vastaranta, M.; Koivisto, T.; Holopainen, M.
Contributor: University of Helsinki, Department of Forest Sciences
University of Helsinki, Department of Forest Sciences
University of Helsinki, Department of Forest Sciences
Date: 2017
Language: eng
Number of pages: 6
Belongs to series: The international archives of the photogrammetry, remote sensing and spatial information sciences
ISSN: 1682-1750
URI: http://hdl.handle.net/10138/299547
Abstract: Novel miniaturized multi- and hyperspectral imaging sensors on board of unmanned aerial vehicles have recently shown great potential in various environmental monitoring and measuring tasks such as precision agriculture and forest management. These systems can be used to collect dense 3D point clouds and spectral information over small areas such as single forest stands or sample plots. Accurate radiometric processing and atmospheric correction is required when data sets from different dates and sensors, collected in varying illumination conditions, are combined. Performance of novel radiometric block adjustment method, developed at Finnish Geospatial Research Institute, is evaluated with multitemporal hyperspectral data set of seedling stands collected during spring and summer 2016. Illumination conditions during campaigns varied from bright to overcast. We use two different methods to produce homogenous image mosaics and hyperspectral point clouds: image-wise relative correction and image-wise relative correction with BRDF. Radiometric datasets are converted to reflectance using reference panels and changes in reflectance spectra is analysed. Tested methods improved image mosaic homogeneity by 5% to 25%. Results show that the evaluated method can produce consistent reflectance mosaics and reflectance spectra shape between different areas and dates. © Authors 2017.
Subject: Automation
Calibration
Environmental management
Forestry
Image enhancement
Radiometry
Reflection
Remote sensing
Spectroscopy, Atmospheric corrections
Environmental Monitoring
HyperSpectral
Hyperspectral imaging sensors
Illumination conditions
Radiometric corrections
Radiometric processing
Unmanned aerial systems, Hyperspectral imaging
1171 Geosciences
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