Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W2, 189-194, 2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.
16 Aug 2013
T. Hakala1, E. Honkavaara1, H. Saari2, J. Mäkynen2, J. Kaivosoja2, L. Pesonen3, and I. Pölönen4 1Finnish Geodetic Institute, Department of Remote Sensing and Photogrammetry, Geodeetinrinne 2, P.O. Box 15, 02431 Masala, Finland
2VTT Technical Research Center of Finland, Photonic Devices and Measurement Solutions, P.O. Box 1000, 02044 VTT, Finland
3MTT Agrifood Research Finland, 31600 Jokioinen, Finland
4Department of Mathematical Information Tech., University of Jyväskylä, P.O. Box 35, 40014, Jyväskylä, Finland
Keywords: Photogrammetry, Geometry, Radiometry, Hyper spectral, Environment, Classification, High-resolution Abstract. Rapidly developing unmanned aerial vehicles (UAV) have provided the remote sensing community with a new rapidly deployable tool for small area monitoring. The progress of small payload UAVs has introduced greater demand for light weight aerial payloads. For applications requiring aerial images, a simple consumer camera provides acceptable data. For applications requiring more detailed spectral information about the surface, a new Fabry-Perot interferometer based spectral imaging technology has been developed. This new technology produces tens of successive images of the scene at different wavelength bands in very short time. These images can be assembled in spectral data cubes with stereoscopic overlaps. On field the weather conditions vary and the UAV operator often has to decide between flight in sub optimal conditions and no flight. Our objective was to investigate methods for quantitative radiometric processing of images taken under varying illumination conditions, thus expanding the range of weather conditions during which successful imaging flights can be made. A new method that is based on insitu measurement of irradiance either in UAV platform or in ground was developed. We tested the methods in a precision agriculture application using realistic data collected in difficult illumination conditions. Internal homogeneity of the original image data (average coefficient of variation in overlapping images) was 0.14–0.18. In the corrected data, the homogeneity was 0.10–0.12 with a correction based on broadband irradiance measured in UAV, 0.07–0.09 with a correction based on spectral irradiance measurement on ground, and 0.05–0.08 with a radiometric block adjustment based on image data. Our results were very promising, indicating that quantitative UAV based remote sensing could be operational in diverse conditions, which is prerequisite for many environmental remote sensing applications.
Conference paper (PDF, 821 KB)

Citation: Hakala, T., Honkavaara, E., Saari, H., Mäkynen, J., Kaivosoja, J., Pesonen, L., and Pölönen, I.: SPECTRAL IMAGING FROM UAVS UNDER VARYING ILLUMINATION CONDITIONS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W2, 189-194, doi:10.5194/isprsarchives-XL-1-W2-189-2013, 2013.

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