publication . Other literature type . Article . Conference object . 2018

PRACTICAL APPROACH FOR HYPERSPECTRAL IMAGE PROCESSING IN PYTHON

Annala, Leevi; Eskelinen, Matti; Hämäläinen, Jyri; Riihinen, Aamos; Pölönen, Ilkka;
Open Access English
  • Published: 30 Apr 2018
  • Country: Finland
Abstract
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 pract...
Subjects
free text keywords: python, kuvankäsittely, koneoppiminen, avoin lähdekoodi, data analysis, hyperspectral imaging, image processing, machine learning, open source, Technology, T, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Related Organizations

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., van der Walt, S., Scho¨nberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E., Yu, T. and the scikitimage contributors, 2014. scikit-image: image processing in Python. PeerJ 2, pp. e453. Source code available at https: //github.com/scikit-image/scikit-image.

Abstract
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 pract...
Subjects
free text keywords: python, kuvankäsittely, koneoppiminen, avoin lähdekoodi, data analysis, hyperspectral imaging, image processing, machine learning, open source, Technology, T, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Related Organizations

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., van der Walt, S., Scho¨nberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E., Yu, T. and the scikitimage contributors, 2014. scikit-image: image processing in Python. PeerJ 2, pp. e453. Source code available at https: //github.com/scikit-image/scikit-image.

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