Crop Classification of Worldview-2 Time Series using Support Vector Machine (SVM) and Random Forest (RF)

Research output: Contribution to conferenceAbstractOther research output

Abstract

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).
Original languageEnglish
Pages3
Number of pages1
Publication statusPublished - 29 Nov 2018
EventNCG symposium 2018 - Wageningen university, Wageningen, Netherlands
Duration: 29 Nov 201829 Nov 2018
https://ncgeo.nl/index.php/nl/actueel/nieuws/item/2781-programma-ncg-symposium-2018

Conference

ConferenceNCG symposium 2018
CountryNetherlands
CityWageningen
Period29/11/1829/11/18
Internet address

Fingerprint

land cover
time series
crop
integrated approach
remote sensing
support vector machine
WorldView

Cite this

@conference{4f8fb105023a4f28b8a368dccc3d9e23,
title = "Crop Classification of Worldview-2 Time Series using Support Vector Machine (SVM) and Random Forest (RF)",
abstract = "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).",
author = "A. Zafari",
year = "2018",
month = "11",
day = "29",
language = "English",
pages = "3",
note = "NCG symposium 2018 ; Conference date: 29-11-2018 Through 29-11-2018",
url = "https://ncgeo.nl/index.php/nl/actueel/nieuws/item/2781-programma-ncg-symposium-2018",

}

Zafari, A 2018, 'Crop Classification of Worldview-2 Time Series using Support Vector Machine (SVM) and Random Forest (RF)' NCG symposium 2018 , Wageningen, Netherlands, 29/11/18 - 29/11/18, pp. 3.

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 conferenceAbstractOther 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 -