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
Julkaistu sarjassa
International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesPäivämäärä
2018Tekijänoikeudet
© 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.
Julkaisija
International Society for Photogrammetry and Remote SensingKonferenssi
Congress of the International Society for Photogrammetry and Remote SensingKuuluu julkaisuun
ISPRS TC III Mid-term Symposium “Developments, Technologies and Applications in Remote Sensing”ISSN Hae Julkaisufoorumista
1682-1750Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/28042967
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
A Do-It-Yourself Hyperspectral Imager Brought to Practice with Open-Source Python
Riihiaho, Kimmo Aukusti; Eskelinen, Matti Aleksanteri; Pölönen, Ilkka (MDPI AG, 2021)Commercial hyperspectral imagers (HSIs) are expensive and thus unobtainable for large audiences or research groups with low funding. In this study, we used an existing do-it-yourself push-broom HSI design for which we ... -
Minimal learning machine in hyperspectral imaging classification
Hakola, Anna-Maria; Pölönen, Ilkka (SPIE, 2020)A hyperspectral (HS) image is typically a stack of frames, where each frame represents the intensity of a different wavelength of light. Each spatial pixel has a spectrum. In the classification of the HS image, each spectrum ... -
SciPy 1.0 : fundamental algorithms for scientific computing in Python
Virtanen, Pauli; Gommers, Ralf; Oliphant, Travis E.; Haberland, Matt, Reddy, Tyler; Cournapeau, David; Burovski, Evgeni; Peterson, Pearu; Weckesser, Warren; Bright, Jonathan; van der Walt, Stéfan J.; Brett, Matthew; Wilson, Joshua; Millman, K. Jarrod; Mayorov, Nikolay; Nelson, Andrew R. J.; Jones, Eric; Kern, Robert; Larson, Eric; Carey, C. J.; Polat, İlhan; Feng, Yu; Moore, Eric W.; VanderPlas, Jake; Laxalde, Denis; Perktold, Josef; Cimrman, Robert; Henriksen, Ian; Quintero, E. A.; Harris, Charles R.; Archibald, Anne M.; Ribeiro, Antônio H.; Pedregosa, Fabian; van Mulbregt, Paul; SciPy 1.0 Contributors (Nature Publishing Group, 2020)SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over ... -
GPAW : An open Python package for electronic structure calculations
Mortensen, Jens Jørgen; Larsen, Ask Hjorth; Kuisma, Mikael; Ivanov, Aleksei V.; Taghizadeh, Alireza; Peterson, Andrew; Haldar, Anubhab; Dohn, Asmus Ougaard; Schäfer, Christian; Jónsson, Elvar Örn; Hermes, Eric D.; Nilsson, Fredrik Andreas; Kastlunger, Georg; Levi, Gianluca; Jónsson, Hannes; Häkkinen, Hannu; Fojt, Jakub; Kangsabanik, Jiban; Sødequist, Joachim; Lehtomäki, Jouko; Heske, Julian; Enkovaara, Jussi; Winther, Kirsten Trøstrup; Dulak, Marcin; Melander, Marko M.; Ovesen, Martin; Louhivuori, Martti; Walter, Michael; Gjerding, Morten; Lopez-Acevedo, Olga; Erhart, Paul; Warmbier, Robert; Würdemann, Rolf; Kaappa, Sami; Latini, Simone; Boland, Tara Maria; Bligaard, Thomas; Skovhus, Thorbjørn; Susi, Toma; Maxson, Tristan; Rossi, Tuomas; Chen, Xi; Schmerwitz, Yorick Leonard A.; Schiøtz, Jakob; Olsen, Thomas; Jacobsen, Karsten Wedel; Thygesen, Kristian Sommer (American Institute of Physics, 2024)We review the GPAW open-source Python package for electronic structure calculations. GPAW is based on the projector-augmented wave method and can solve the self-consistent density functional theory (DFT) equations using ... -
Approaches and challenges of automatic vulnerability classification using natural language processing and machine learning techniques
Jormakka, Ossi (2019)Automatisoitu haavoittuvuuksien etsiminen ja haavoittuvuuksien yksityiskohtien ennustaminen voi auttaa asiantuntijoita priorisoimaan ohjelmistovirheitä, joka voi johtaa nopeampaan virheenkorjaukseen. Tässä työssä käytettiin ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.