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

Visa fullständig post



Permalänk

http://hdl.handle.net/10138/324454

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

Titel: Utilizing Sentinel-1A Radar Images for Large-Area Land Cover Mapping with Machine-learning Methods
Författare: Imangholiloo, Mohammad; Rasinmaki, Jussi; Rauste, Yrjo; Holopainen, Markus
Medarbetare: University of Helsinki, Laboratory of Forest Resources Management and Geo-information Science
University of Helsinki, Department of Forest Sciences
Datum: 2019-03
Språk: eng
Sidantal: 13
Tillhör serie: Canadian journal of remote sensing
ISSN: 0703-8992
Permanenta länken (URI): http://hdl.handle.net/10138/324454
Abstrakt: 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.
Subject: 4112 Forestry
RANDOM FORESTS
SAR
Licens:


Filer under denna titel

Totalt antal nerladdningar: Laddar...

Filer Storlek Format Granska
manuscript_Iman ... sed_2nd_20190621_Clean.pdf 1.511Mb PDF Granska/Öppna

Detta dokument registreras i samling:

Visa fullständig post