Generation and application of hyperspectral 3D plant models: methods and challenges
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Hyperspectral imaging sensors have been introduced for measuring the health status of plants. Recently, they also have been used for close-range sensing of plant canopies with a highly complex architecture. However, the complex geometry of plants and their interaction with the illumination setting severely affect the spectral information obtained. Furthermore, the spatial component of analysis results gain in importance as higher plants are represented by multiple plant organs as leaves, stems and seed pods. The combination of hyperspectral images and 3D point clouds is a promising approach to face these problems. We present the generation and application of hyperspectral 3D plant models as a new, interesting application field for computer vision with a variety of challenging tasks. We sum up a geometric calibration method for hyperspectral pushbroom cameras using a reference object for the combination of spectral and spatial information. Furthermore, we show exemplarily new calibration and analysis methods enabled by the hyperspectral 3D models in an experiment with sugar beet plants. An improved normalization, a comparison of image and 3D analysis and the density estimation of infected surface points underline some of the new capabilities gained using this new data type. Based on such hyperspectral 3D models the effects of plant geometry and sensor configuration can be quantified and modeled. In future, reflectance models can be used to remove or weaken the geometry-related effects in hyperspectral images and, therefore, have the potential to improve automated plant phenotyping significantly.
KeywordsHyperspectral 3D scanning Close range Plant phenotyping Modeling Sensor fusion
The authors acknowledge the funding of the CROP.SENSe.net project in the context of Ziel 2-Programms NRW 2007–2013 “Regionale Wettbewerbsfähigkeit und Beschäftigung (EFRE)” by the Ministry for Innovation, Science and Research (MIWF) of the state North Rhine Westphalia (NRW) and European Union Funds for regional development (EFRE) (005-1103-0018) during the preparation of the manuscript.
- 1.Bannehr, L., Luhmann, T., Piechel, J., Roelfs, T., Schmidt, A.: Extracting roof parameters and heat bridges over the city of Oldenburg from hyperspectral, thermal, and airborne laser scanning data. ISPRS Int Arch Photogramm Remote Sens Spat Inf Sci 3819, 17–22 (2011)Google Scholar
- 2.Bareth, G., Aasen, H., Bendig, J., Gnyp, M.L., Bolten, A., Jung, A., Michels, R., Soukkamäki, J.: Low-weight and UAV-based hyperspectral full-frame cameras for monitoring crops: spectral comparison with portable spectroradiometer measurements. Photogrammetrie-Fernerkundung-Geoinformation 2015(1), 69–79 (2015)Google Scholar
- 3.Behmann, J., Mahlein, A.K., Paulus, S., Kuhlmann, H., Oerke, E.C., Plümer, L.: Generation and application of hyperspectral 3d plant models. In: Computer Vision-ECCV 2014 Workshops, pp. 117–130. Springer, Berlin (2014)Google Scholar
- 13.Dupuis, J., Kuhlmann, H.: High-precision surface inspection: uncertainty evaluation within an accuracy range of 15\(\mu \)m with triangulation-based laser line scanners. J. Appl. Geod. 8(2), 109–118 (2014)Google Scholar
- 15.Grahn, H., Geladi, P.: Techniques and Applications of Hyperspectral Image Analysis. Wiley, New York (2007)Google Scholar
- 22.Liang, J., Zia, A., Zhou, J., Sirault, X.: 3D plant modelling via hyperspectral imaging. In: 2013 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 172–177 (2013)Google Scholar
- 25.Mason, J.C., Handscomb, D.C.: Chebyshev Polynomials. CRC Press, Boca Raton (2002)Google Scholar
- 33.Schöler, F., Steinhage, V.: Towards an automated 3D reconstruction of plant architecture. In: Proceedings of the 4th International Conference on Applications of Graph Transformations with Industrial Relevance, pp. 51–64. Springer, Berlin (2012)Google Scholar
- 34.Tilly, N., Hoffmeister, D., Liang, H., Cao, Q., Liu, Y., Miao, Y., Bareth, G.: Evaluation of terrestrial laser scanning for rice growth monitoring. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS Congress, Melbourne, Australia XXXIX, pp. 351–356 (2012)Google Scholar