Abstract
Original language | English |
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Pages | 3 |
Number of pages | 1 |
Publication status | Published - 29 Nov 2018 |
Event | NCG symposium 2018 - Wageningen university, Wageningen, Netherlands Duration: 29 Nov 2018 → 29 Nov 2018 https://ncgeo.nl/index.php/nl/actueel/nieuws/item/2781-programma-ncg-symposium-2018 |
Conference
Conference | NCG symposium 2018 |
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Country | Netherlands |
City | Wageningen |
Period | 29/11/18 → 29/11/18 |
Internet address |
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Crop Classification of Worldview-2 Time Series using Support Vector Machine (SVM) and Random Forest (RF). / Zafari, A.
2018. 3 Abstract from NCG symposium 2018 , Wageningen, Netherlands.Research output: Contribution to conference › Abstract › Other research output
TY - CONF
T1 - Crop Classification of Worldview-2 Time Series using Support Vector Machine (SVM) and Random Forest (RF)
AU - Zafari, A.
PY - 2018/11/29
Y1 - 2018/11/29
N2 - Land cover mapping using high dimensional data is a common task in remote sensing. Random Forest (RF) and Support Vector Machine (SVM) are often reported in the literature as efficient classifiers for land cover mapping, particularly, in dealing with high-dimensional data. In this research, the possibility of crop classification on time series of Worldview2 images is evaluated in an integrated approach using two most acknowledged supervised learner including random forest (RF) and support vector machine (SVM).
AB - Land cover mapping using high dimensional data is a common task in remote sensing. Random Forest (RF) and Support Vector Machine (SVM) are often reported in the literature as efficient classifiers for land cover mapping, particularly, in dealing with high-dimensional data. In this research, the possibility of crop classification on time series of Worldview2 images is evaluated in an integrated approach using two most acknowledged supervised learner including random forest (RF) and support vector machine (SVM).
UR - https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2018/pres/zafari_cro_abs.pdf
M3 - Abstract
SP - 3
ER -