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	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

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Zafari, A. (2018). Crop Classification of Worldview-2 Time Series using Support Vector Machine (SVM) and Random Forest (RF). 3. Abstract from NCG symposium 2018 , Wageningen, Netherlands.

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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).",

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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).

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