HyperBlend : Simulating Spectral Reflectance and Transmittance of Leaf Tissue with Blender
Riihiaho, K. A., Rossi, T., & Pölönen, I. (2022). HyperBlend : Simulating Spectral Reflectance and Transmittance of Leaf Tissue with Blender. In J. Jiang, A. Shaker, & H. Zhang (Eds.), XXIV ISPRS Congress “Imaging today, foreseeing tomorrow”, Commission III (V-3-2022, pp. 471-476). Copernicus Publications. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. https://doi.org/10.5194/isprs-annals-V-3-2022-471-2022
Date
2022Discipline
Computing, Information Technology and MathematicsTutkintokoulutusTietotekniikkaLaskennallinen tiedeComputing, Information Technology and MathematicsDegree EducationMathematical Information TechnologyComputational ScienceCopyright
© Author(s) 2022
Remotely sensing vegetation condition and health hazards requires modeling the connection of plants’ biophysical and biochemical parameters to their spectral response. Even though many models exist already, the field suffers from lack of access to program code. In this study, we will assess the feasibility of open-source 3D-modeling and rendering software Blender in simulating hyperspectral reflectance and transmittance of leaf tissue to serve as a base for a more advanced large-scale simulator. This is the first phase of a larger HyperBlend project, which will provide a fully open-source, canopy scale leaf optical properties model for simulating remotely sensed hyperspectral images. Test results of the current HyperBlend model show good agreement with real-world measurements with root mean squared error around 1‰. The program code is available at https://github.com/silmae/ hyperblend.
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Copernicus PublicationsConference
International Society for Photogrammetry and Remote Sensing CongressIs part of publication
XXIV ISPRS Congress “Imaging today, foreseeing tomorrow”, Commission IIIISSN Search the Publication Forum
2194-9042Keywords
Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/150900468
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Research Council of FinlandFunding program(s)
Academy Project, AoFAdditional information about funding
This study was funded by Academy of Finland (327862).License
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