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, . . . K. Komp (Eds.), ISPRS TC III Mid-term Symposium “Developments, Technologies and Applications in Remote Sensing” (pp. 45-52). International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3. International Society for Photogrammetry and Remote Sensing. doi:10.5194/isprs-archives-XLII-3-45-2018
Published inInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
© 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.