Utilizing Sentinel-1A Radar Images for Large-Area Land Cover Mapping with Machine-learning Methods

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dc.contributor University of Helsinki, Laboratory of Forest Resources Management and Geo-information Science en
dc.contributor University of Helsinki, Department of Forest Sciences en
dc.contributor.author Imangholiloo, Mohammad
dc.contributor.author Rasinmaki, Jussi
dc.contributor.author Rauste, Yrjo
dc.contributor.author Holopainen, Markus
dc.date.accessioned 2021-01-13T08:17:01Z
dc.date.available 2021-01-13T08:17:01Z
dc.date.issued 2019-03
dc.identifier.citation Imangholiloo , M , Rasinmaki , J , Rauste , Y & Holopainen , M 2019 , ' Utilizing Sentinel-1A Radar Images for Large-Area Land Cover Mapping with Machine-learning Methods ' , Canadian journal of remote sensing , vol. 45 , no. 2 , pp. 163-175 . https://doi.org/10.1080/07038992.2019.1635877 en
dc.identifier.issn 0703-8992
dc.identifier.other PURE: 126082810
dc.identifier.other PURE UUID: 77bf60b7-ea1a-4206-ae36-e5be03829ac2
dc.identifier.other WOS: 000476016000001
dc.identifier.uri http://hdl.handle.net/10138/324454
dc.description.abstract Land use and land cover maps are vital sources of information for many applications. Recently, using high-resolution and open-access satellite images has become a preferred method for mapping land cover, especially over large areas. This study was designed to map the land cover and agricultural fields of a large area using Sentinel-1A synthetic aperture radar (SAR) images. Seven machine-learning methods were employed for image analyses. The Random Forest classifier algorithm outperformed the other machine-learning methods in the training step; thus, we selected it for further use and tuned its parameters. After several image processing steps, we classified the final image into 23 land cover classes and achieved an overall accuracy of 42% for all classes, and 57% for agricultural fields. This research note highlights some characteristics and advantages of using Sentinel-1A images and provides novel methods for nation-wide large-area mapping applications. en
dc.format.extent 13
dc.language.iso eng
dc.relation.ispartof Canadian journal of remote sensing
dc.rights en
dc.subject 4112 Forestry en
dc.subject RANDOM FORESTS en
dc.subject SAR en
dc.title Utilizing Sentinel-1A Radar Images for Large-Area Land Cover Mapping with Machine-learning Methods en
dc.type Article
dc.description.version Peer reviewed
dc.identifier.doi https://doi.org/10.1080/07038992.2019.1635877
dc.type.uri info:eu-repo/semantics/other
dc.type.uri info:eu-repo/semantics/acceptedVersion
dc.contributor.pbl
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