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

Research output: Contribution to conferenceAbstract

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

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