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

A. Zafari

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 language	English
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
Country/Territory	Netherlands
City	Wageningen
Period	29/11/18 → 29/11/18
Internet address	https://ncgeo.nl/index.php/nl/actueel/nieuws/item/2781-programma-ncg-symposium-2018

Cite this

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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 = nov,

day = "29",

language = "English",

pages = "3",

note = "NCG symposium 2018 ; Conference date: 29-11-2018 Through 29-11-2018",

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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

T2 - NCG symposium 2018

Y2 - 29 November 2018 through 29 November 2018

ER -

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

Abstract

Conference

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Fingerprint

Integrating support vector machines and random forests to classify crops in time series of Worldview-2 images

Cite this

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

Abstract

Conference

Other files and links

Fingerprint

Research output

Integrating support vector machines and random forests to classify crops in time series of Worldview-2 images

Cite this