INFLUENCE of the VIEWING GEOMETRY WITHIN HYPERSPECTRAL IMAGES RETRIEVED from UAV SNAPSHOT CAMERAS

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Volumn 3, Issue , 2016, Pages 257-261

  Aasen, Helge ^a  

^a UNIVERSITY OF COLOGNE   (Germany)

Author keywords

angular effects; barley; BRDF; image frame camera; vegetation index

Indexed keywords

EID: 85048906679     PISSN: 21949042     EISSN: 21949050     Source Type: Conference Proceeding    
DOI: 10.5194/isprs-annals-III-7-257-2016     Document Type: Conference Paper

References (20)

1. Aasen, H., Bendig, J., Bolten, A., Bennertz, S., Willkomm, M., Bareth, G., 2014a. Introduction and preliminary results of a calibration for full-frame hyperspectral cameras to monitor agricultural crops with UAVs, in: ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. pp. 1-8.
2. Aasen, H., Bolten, A., in review. Multi-temporal monitoring of agricultural crops with high-resolution 3D hyperspectral digital surface models in comparison with ground observations. Remote Sens. Environ.
3. Aasen, H., Burkart, A., Bolten, A., Bareth, G., 2015. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS J. Photogramm. Remote Sens. 108, 245-259.
4. Aasen, H., Gnyp, M.L., Miao, Y., Bareth, G., 2014b. Automated Hyperspectral Vegetation Index Retrieval from Multiple Correlation Matrices with HyperCor. Photogramm. Eng. Remote Sens. 80, 785-795.
5. Bareth, G., Aasen, H., Bendig, J., Gnyp, M.L., Bolten, A., Jung, A., Michels, R., Soukkamäki, J., 2015. Low-weight and UAV-based Hyperspectral Full-frame Cameras for Monitoring Crops: Spectral Comparison with Portable Spectroradiometer Measurements. Photogramm.-Fernerkund.-Geoinformation 2015, 69-79.
6. Burkart, A., Aasen, H., Alonso, L., Menz, G., Bareth, G., Rascher, U., 2015. Angular Dependency of Hyperspectral Measurements over Wheat Characterized by a Novel UAV Based Goniometer. Remote Sens. 7, 725-746.
7. Constantin, D., Rehak, M., Akhtman, Y., Liebisch, F., 2015. Detection of crop properties by means of hyperspectral remote sensing from a micro UAV, in: 9th EARSeL SIG Imaging Spectroscopy Workshop.
8. Gatebe, C.K., 2003. Airborne spectral measurements of surface-atmosphere anisotropy for several surfaces and ecosystems over southern Africa. J. Geophys. Res. 108.
9. Gerstl, S.A.W., Simmer, C., 1986. Radiation physics and modelling for off-nadir satellite-sensing of non-Lambertian surfaces. Remote Sens. Environ. 20, 1-29.
10. Honkavaara, E., Saari, H., Kaivosoja, J., Pölönen, I., Hakala, T., Litkey, P., Mäkynen, J., Pesonen, L., 2013. Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture. Remote Sens. 5, 5006-5039.
11. Jones, H.G., Vaughan, R.A., 2010. Remote sensing of vegetation: principles, techniques, and applications. Oxford University Press, Oxford; New York.
12. Koukal, T., Atzberger, C., Schneider, W., 2014. Evaluation of semi-empirical BRDF models inverted against multi-angle data from a digital airborne frame camera for enhancing forest type classification. Remote Sens. Environ. 151, 27-43.
13. Küster, T., Spengler, D., Barczi, J.-F., Segl, K., Hostert, P., Kaufmann, H., 2014. Simulation of Multitemporal and Hyperspectral Vegetation Canopy Bidirectional Reflectance Using Detailed Virtual 3-D Canopy Models. IEEE Trans. Geosci. Remote Sens. 52, 2096-2108.
14. Nicodemus, F.E., Richmond, J.C., Hsia, J.J., Ginsberg, I.W., Limperis, T., 1977. Geometrical considerations and nomenclature for reflectance. National Bureau of Standards, Washington DC, p. 67.
15. Sandmeier, S.R., Itten, K., others, 1999. A field goniometer system (FIGOS) for acquisition of hyperspectral BRDF data. Geosci. Remote Sens. IEEE Trans. On 37, 978-986.
16. Schaepman-Strub, G., Schaepman, M.E., Painter, T.H., Dangel, S., Martonchik, J.V., 2006. Reflectance quantities in optical remote sensing - definitions and case studies. Remote Sens. Environ. 103, 27-42. 2
17. Schläpfer, D., Richter, R., Feingersh, T., 2015. Operational BRDF Effects Correction for Wide-Field-of-View Optical Scanners (BREFCOR). IEEE Trans. Geosci. Remote Sens. 53, 1855-1864.
18. Thenkabail, P.S., Lyon, J.G., Huete, A. (Eds.), 2012. Hyperspectral remote sensing of vegetation. CRC Press, Boca Raton, FL.
19. Tilly, N., Aasen, H., Bareth, G., 2015. Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass. Remote Sens. 7, 11449-11480.
20. Verrelst, J., Schaepman, M.E., Koetz, B., Kneubühler, M., 2008. Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data. Remote Sens. Environ. 112, 2341-2353.