Utilizing Sentinel-1A SAR Images for Land Cover Mapping with Machine Learning Methods

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dc.contributor Helsingin yliopisto, Maatalous-metsätieteellinen tiedekunta, Metsätieteiden laitos fi
dc.contributor University of Helsinki, Faculty of Agriculture and Forestry, Department of Forest Sciences en
dc.contributor Helsingfors universitet, Agrikultur- och forstvetenskapliga fakulteten, Institutionen för skogsvetenskaper sv
dc.contributor.author Mohammad, Imangholiloo
dc.date.issued 2017
dc.identifier.uri URN:NBN:fi:hulib-201712195979
dc.identifier.uri http://hdl.handle.net/10138/229816
dc.description.abstract Land use and land cover maps are vital sources of information for many uses. Recently, the use of high resolution and open access satellite images are being preferred for mapping large areas. Sentinel satellites exhibit such valuable traits. This study was designed to analyze the potential of Sentinel-1A SAR images for land use mapping in Pakistan. Machine learning methods were employed for image analysis. Random forest classifier algorithm performed significantly better than others in the training step. Thus, we took the model for tuning parameters. After several image processing steps, we classified the final image to 23 classes and achieved 42 % of an overall accuracy. The present study showed the potential advantages of using Sentinel-1 images in land use mapping besides highlighting some characteristics of Sentinel-1A images. This study also compares the results with an earlier study using Landsat-8 optical multispectral images over the same area. Similar to the prior study, overestimation in dominant classes and underestimation in rare classes were observed. The method and findings of this study could be beneficial for future studies in the use of Sentinel-1A images for land use/cover mapping over large areas. en
dc.language.iso en
dc.publisher Helsingin yliopisto fi
dc.publisher University of Helsinki en
dc.publisher Helsingfors universitet sv
dc.subject Sentinel-1A en
dc.subject SAR en
dc.subject land use and land cover en
dc.subject mapping en
dc.subject machine learning en
dc.subject Random forest en
dc.title Utilizing Sentinel-1A SAR Images for Land Cover Mapping with Machine Learning Methods en
dc.type.ontasot pro gradu -tutkielmat fi
dc.type.ontasot master's thesis en
dc.type.ontasot pro gradu-avhandlingar sv
dc.subject.discipline Metsien ekologia ja käyttö fi
dc.subject.discipline Forest Ecology and Management en
dc.subject.discipline skoglig ekologi och resurshushållning sv
dct.identifier.urn URN:NBN:fi:hulib-201712195979

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