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

Mohammad Imangholiloo (Corresponding Author), Jussi Rasinmäki, Yrjö Rauste, Markus Holopainen

    Research output: Contribution to journalArticleScientificpeer-review

    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.

    Original languageEnglish
    Pages (from-to)163-175
    Number of pages13
    JournalCanadian Journal of Remote Sensing
    Volume45
    Issue number2
    DOIs
    Publication statusPublished - 2019
    MoE publication typeA1 Journal article-refereed

    Fingerprint

    land cover
    radar
    image processing
    synthetic aperture radar
    land use
    machine learning
    method

    Keywords

    • radar
    • land cover

    Cite this

    Imangholiloo, Mohammad ; Rasinmäki, Jussi ; Rauste, Yrjö ; Holopainen, Markus. / Utilizing Sentinel-1A Radar Images for Large-Area Land Cover Mapping with Machine-learning Methods. In: Canadian Journal of Remote Sensing. 2019 ; Vol. 45, No. 2. pp. 163-175.
    @article{ffb4ac7f6ea2491dbe29f604e1379dc1,
    title = "Utilizing Sentinel-1A Radar Images for Large-Area Land Cover Mapping with Machine-learning Methods",
    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.",
    keywords = "radar, land cover",
    author = "Mohammad Imangholiloo and Jussi Rasinm{\"a}ki and Yrj{\"o} Rauste and Markus Holopainen",
    year = "2019",
    doi = "10.1080/07038992.2019.1635877",
    language = "English",
    volume = "45",
    pages = "163--175",
    journal = "Canadian Journal of Remote Sensing",
    issn = "0703-8992",
    publisher = "Taylor & Francis",
    number = "2",

    }

    Utilizing Sentinel-1A Radar Images for Large-Area Land Cover Mapping with Machine-learning Methods. / Imangholiloo, Mohammad (Corresponding Author); Rasinmäki, Jussi; Rauste, Yrjö; Holopainen, Markus.

    In: Canadian Journal of Remote Sensing, Vol. 45, No. 2, 2019, p. 163-175.

    Research output: Contribution to journalArticleScientificpeer-review

    TY - JOUR

    T1 - Utilizing Sentinel-1A Radar Images for Large-Area Land Cover Mapping with Machine-learning Methods

    AU - Imangholiloo, Mohammad

    AU - Rasinmäki, Jussi

    AU - Rauste, Yrjö

    AU - Holopainen, Markus

    PY - 2019

    Y1 - 2019

    N2 - 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.

    AB - 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.

    KW - radar

    KW - land cover

    UR - http://www.scopus.com/inward/record.url?scp=85068892294&partnerID=8YFLogxK

    U2 - 10.1080/07038992.2019.1635877

    DO - 10.1080/07038992.2019.1635877

    M3 - Article

    AN - SCOPUS:85068892294

    VL - 45

    SP - 163

    EP - 175

    JO - Canadian Journal of Remote Sensing

    JF - Canadian Journal of Remote Sensing

    SN - 0703-8992

    IS - 2

    ER -