Practical Approach for Hyperspectral Image Processing in Python
Annala, L., Eskelinen, M., Hämäläinen, J., Riihinen, A., & Pölönen, I. (2018). Practical Approach for Hyperspectral Image Processing in Python. In J. Jiang, A. Shaker, H. Zhang, X. Liang, B. Osmanoglu, U. Soergel, E. Honkavaara, M. Scaioni, J. Zhang, A. Peled, L. Wu, R. Li, M. Yoshimura, K. Di, T. J. Tanzi, H. M. Abdulmuttalib, F. S. Faruque, U. Stilla, & K. Komp (Eds.), ISPRS TC III Mid-term Symposium “Developments, Technologies and Applications in Remote Sensing” (pp. 45-52). International Society for Photogrammetry and Remote Sensing. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3. https://doi.org/10.5194/isprs-archives-XLII-3-45-2018
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International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesDate
2018Copyright
© Authors 2018
Python is a very popular programming language among data scientists around the world. Python can also be used in hyperspectral
data analysis. There are some toolboxes designed for spectral imaging, such as Spectral Python and HyperSpy, but there is a need for
analysis pipeline, which is easy to use and agile for different solutions. We propose a Python pipeline which is built on packages xarray,
Holoviews and scikit-learn. We have developed some of own tools, MaskAccessor, VisualisorAccessor and a spectral index library.
They also fulfill our goal of easy and agile data processing. In this paper we will present our processing pipeline and demonstrate it in
practice.
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International Society for Photogrammetry and Remote SensingConference
Congress of the International Society for Photogrammetry and Remote SensingIs part of publication
ISPRS TC III Mid-term Symposium “Developments, Technologies and Applications in Remote Sensing”ISSN Search the Publication Forum
1682-1750Keywords
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