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Pregled bibliografske jedinice broj: 1076315

Integration Of Multitemporal Sentinel-1 And Sentinel-2 Imagery For Land-Cover Classification Using Machine Learning Methods


Dobrinić, Dino; Medak, Damir; Gašparović, Mateo
Integration Of Multitemporal Sentinel-1 And Sentinel-2 Imagery For Land-Cover Classification Using Machine Learning Methods // The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B1-2020
Nice, Francuska, 2020. str. 91-98 doi:10.5194/isprs-archives-XLIII-B1-2020-91-2020 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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Naslov
Integration Of Multitemporal Sentinel-1 And Sentinel-2 Imagery For Land-Cover Classification Using Machine Learning Methods

Autori
Dobrinić, Dino ; Medak, Damir ; Gašparović, Mateo

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B1-2020 / - , 2020, 91-98

Skup
XXIV ISPRS Congress 2020

Mjesto i datum
Nice, Francuska, 14-20.06.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Land-Cover Classification ; Multitemporal Analysis ; Random Forest ; SAR ; Sentinel-1 ; Sentinel-2 ; XGBoost

Sažetak
Using space-borne remote sensing data is widely used for land-cover classification (LCC) due to its ability to provide a big amount of data with a regular temporal revisit time. In recent years, optical and synthetic aperture radar (SAR) imagery have become available for free, and their integration in time series have improved LCC. This research evaluates the classification accuracy using multitemporal (MT) Sentinel-1 (S1) and Sentinel-2 (S2) imagery. Pixel-based LCC is made for S1 and S2 imagery, and for a combination of both datasets with Random Forest (RF) and Extreme Gradient Boosting (XGBoost ; XGB). The extent of the study area, is located in the south-east of France, in Lyon. Regardless of LCC using single-date or MT data, the highest classification results were achieved with integrated S1 and S2 imagery and XGB method, whereas overall accuracy (OA) and Kappa coefficient (Kappa) increased from 85.51% to 91.09%, and from 0.81 to 0.88, respectively. Furthermore, the integration of MT imagery significantly improved the classification of urban areas and reduced misclassification between forest and low vegetation. In this paper, in terms of the pixel-based classification, XGB produced slightly better results than RF, and outperformed it in terms of computational time. This research improved LCC with integration of radar and optical MT imagery, which can be useful for areas hampered by a frequent cloud cover. Future work should use the aforementioned data for specific applications in remote sensing, as well as evaluate the classification performance with different approaches, such as neural networks or deep learning.

Izvorni jezik
Engleski

Znanstvena područja
Geodezija



POVEZANOST RADA


Projekti:
RS4ENVIRO
HRZZ-IP-2016-06-5621 - Geoprostorno praćenje zelene infrastrukture na temelju terestričkih, zračnih i satelitskih snimaka (GEMINI) (Medak, Damir, HRZZ - 2016-06) ( POIROT)

Ustanove:
Geodetski fakultet, Zagreb

Profili:

Avatar Url Damir Medak (autor)

Avatar Url Dino Dobrinić (autor)

Avatar Url Mateo Gašparović (autor)

Citiraj ovu publikaciju

Dobrinić, Dino; Medak, Damir; Gašparović, Mateo
Integration Of Multitemporal Sentinel-1 And Sentinel-2 Imagery For Land-Cover Classification Using Machine Learning Methods // The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B1-2020
Nice, Francuska, 2020. str. 91-98 doi:10.5194/isprs-archives-XLIII-B1-2020-91-2020 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Dobrinić, D., Medak, D. & Gašparović, M. (2020) Integration Of Multitemporal Sentinel-1 And Sentinel-2 Imagery For Land-Cover Classification Using Machine Learning Methods. U: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B1-2020 doi:10.5194/isprs-archives-XLIII-B1-2020-91-2020.
@article{article, year = {2020}, pages = {91-98}, DOI = {10.5194/isprs-archives-XLIII-B1-2020-91-2020}, keywords = {Land-Cover Classification, Multitemporal Analysis, Random Forest, SAR, Sentinel-1, Sentinel-2, XGBoost}, doi = {10.5194/isprs-archives-XLIII-B1-2020-91-2020}, title = {Integration Of Multitemporal Sentinel-1 And Sentinel-2 Imagery For Land-Cover Classification Using Machine Learning Methods}, keyword = {Land-Cover Classification, Multitemporal Analysis, Random Forest, SAR, Sentinel-1, Sentinel-2, XGBoost}, publisherplace = {Nice, Francuska} }
@article{article, year = {2020}, pages = {91-98}, DOI = {10.5194/isprs-archives-XLIII-B1-2020-91-2020}, keywords = {Land-Cover Classification, Multitemporal Analysis, Random Forest, SAR, Sentinel-1, Sentinel-2, XGBoost}, doi = {10.5194/isprs-archives-XLIII-B1-2020-91-2020}, title = {Integration Of Multitemporal Sentinel-1 And Sentinel-2 Imagery For Land-Cover Classification Using Machine Learning Methods}, keyword = {Land-Cover Classification, Multitemporal Analysis, Random Forest, SAR, Sentinel-1, Sentinel-2, XGBoost}, publisherplace = {Nice, Francuska} }

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