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Pregled bibliografske jedinice broj: 1061400

Evaluating Different Machine Learning Methods on RapidEye and PlanetScope Satellite Imagery


Kranjčić, Nikola; Medak, Damir
Evaluating Different Machine Learning Methods on RapidEye and PlanetScope Satellite Imagery // Geodetski list, 74 (97) (2020), 1; 1-18 (međunarodna recenzija, članak, znanstveni)


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Naslov
Evaluating Different Machine Learning Methods on RapidEye and PlanetScope Satellite Imagery

Autori
Kranjčić, Nikola ; Medak, Damir

Izvornik
Geodetski list (0016-710X) 74 (97) (2020), 1; 1-18

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
support vector machines ; artificial neural network ; naive Bayes ; random forest ; RapidEye ; PlanetScope

Sažetak
Since the first satellite imagery of RapidEye and PlanetScope became available, numerous studies have been conducted. However, only a few authors have focused on evaluating the accuracy of more than two machine learning methods in land cover classification. This paper evaluates the accuracy of four different machine learning methods, namely: support vector machine, artificial neural network, naive Bayes, and random forest. All analysis was conducted on cities in Croatia, Varaždin and Osijek. On Varaždin area on RapidEye satellite imagery support vector machine achieved overall kappa value 0.80, artificial neural network 0.37, naive Bayes 0.84 and random forest 0.76. On Varaždin area on PlanetScope satellite imagery support vector machine achieved overall kappa value 0.77, artificial neural network 0.38, naive Bayes 0.76 and random forest 0.75. On Osijek area on RapidEye satellite imagery support vector machine achieved overall kappa value 0.75, artificial neural network 0.36, naive Bayes 0.85 and random forest 0.76. On Osijek area on Planet- Scope satellite imagery support vector machine achieved overall kappa value 0.64, artificial neural network 0.23, naive Bayes 0.72 and random forest 0.63. Performance time of each method is also evaluated. Naive Bayes and random forest have best performance time in every scenario.

Izvorni jezik
Engleski

Znanstvena područja
Geodezija



POVEZANOST RADA


Projekt / tema
HRZZ-IP-2016-06-5621 - Geoprostorno praćenje zelene infrastrukture na temelju terestričkih, zračnih i satelitskih snimaka (Damir Medak, )

Ustanove
Geodetski fakultet, Zagreb,
Geotehnički fakultet, Varaždin

Profili:

Avatar Url Nikola Kranjčić (autor)

Avatar Url Damir Medak (autor)

Citiraj ovu publikaciju

Kranjčić, Nikola; Medak, Damir
Evaluating Different Machine Learning Methods on RapidEye and PlanetScope Satellite Imagery // Geodetski list, 74 (97) (2020), 1; 1-18 (međunarodna recenzija, članak, znanstveni)
Kranjčić, N. & Medak, D. (2020) Evaluating Different Machine Learning Methods on RapidEye and PlanetScope Satellite Imagery. Geodetski list, 74 (97) (1), 1-18.
@article{article, year = {2020}, pages = {1-18}, keywords = {support vector machines, artificial neural network, naive Bayes, random forest, RapidEye, PlanetScope}, journal = {Geodetski list}, volume = {74 (97)}, number = {1}, issn = {0016-710X}, title = {Evaluating Different Machine Learning Methods on RapidEye and PlanetScope Satellite Imagery}, keyword = {support vector machines, artificial neural network, naive Bayes, random forest, RapidEye, PlanetScope} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Emerging Sources Citation Index (ESCI)
  • Scopus