Remote sensing of 3-D geometry and surface moisture of a peat production area using hyperspectral frame cameras in visible to short-wave infrared spectral ranges onboard a small Unmanned Airborne Vehicle (UAV)

Eija Honkavaara, Matti A. Eskelinen, Ilkka Pölönen, Heikki Saari, Harri Ojanen, Rami Mannila, Christer Holmlund, Teemu Hakala, Paula Litkey, tomi Rosnell, Niko Viljanen, Merja Pulkkanen

    Research output: Contribution to journalArticleScientificpeer-review

    34 Citations (Scopus)

    Abstract

    Miniaturized hyperspectral imaging sensors are becoming available to small unmanned airborne vehicle (UAV) platforms. Imaging concepts based on frame format offer an attractive alternative to conventional hyperspectral pushbroom scanners because they enable enhanced processing and interpretation potential by allowing for acquisition of the 3-D geometry of the object and multiple object views together with the hyperspectral reflectance signatures. The objective of this investigation was to study the performance of novel visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral frame cameras based on a tunable Fabry-Pérot interferometer (FPI) in measuring a 3-D digital surface model and the surface moisture of a peat production area. UAV image blocks were captured with ground sample distances (GSDs) of 15, 9.5, and 2.5 cm with the SWIR, VNIR, and consumer RGB cameras, respectively. Georeferencing showed consistent behavior, with accuracy levels better than GSD for the FPI cameras. The best accuracy in moisture estimation was obtained when using the reflectance difference of the SWIR band at 1246 nm and of the VNIR band at 859 nm, which gave a root mean square error (rmse) of 5.21 pp (pp is the mass fraction in percentage points) and a normalized rmse of 7.61%. The results are encouraging, indicating that UAV-based remote sensing could significantly improve the efficiency and environmental safety aspects of peat production.
    Original languageEnglish
    Pages (from-to)5440-5454
    JournalIEEE Transactions on Geoscience and Remote Sensing
    Volume54
    Issue number9
    DOIs
    Publication statusPublished - 2016
    MoE publication typeA1 Journal article-refereed

    Fingerprint

    Peat
    peat
    Remote sensing
    near infrared
    Moisture
    Cameras
    moisture
    Infrared radiation
    remote sensing
    interferometer
    geometry
    Geometry
    reflectance
    scanner
    Mean square error
    Interferometers
    safety
    sensor
    vehicle
    Imaging techniques

    Keywords

    • calibration
    • geographic information system
    • geometry
    • image classification
    • radiometry
    • remote sensing
    • remotely piloted aircraft
    • spectroscopy
    • stereo vision

    Cite this

    @article{893c6f93b8a94551b5b720b76cc39c02,
    title = "Remote sensing of 3-D geometry and surface moisture of a peat production area using hyperspectral frame cameras in visible to short-wave infrared spectral ranges onboard a small Unmanned Airborne Vehicle (UAV)",
    abstract = "Miniaturized hyperspectral imaging sensors are becoming available to small unmanned airborne vehicle (UAV) platforms. Imaging concepts based on frame format offer an attractive alternative to conventional hyperspectral pushbroom scanners because they enable enhanced processing and interpretation potential by allowing for acquisition of the 3-D geometry of the object and multiple object views together with the hyperspectral reflectance signatures. The objective of this investigation was to study the performance of novel visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral frame cameras based on a tunable Fabry-P{\'e}rot interferometer (FPI) in measuring a 3-D digital surface model and the surface moisture of a peat production area. UAV image blocks were captured with ground sample distances (GSDs) of 15, 9.5, and 2.5 cm with the SWIR, VNIR, and consumer RGB cameras, respectively. Georeferencing showed consistent behavior, with accuracy levels better than GSD for the FPI cameras. The best accuracy in moisture estimation was obtained when using the reflectance difference of the SWIR band at 1246 nm and of the VNIR band at 859 nm, which gave a root mean square error (rmse) of 5.21 pp (pp is the mass fraction in percentage points) and a normalized rmse of 7.61{\%}. The results are encouraging, indicating that UAV-based remote sensing could significantly improve the efficiency and environmental safety aspects of peat production.",
    keywords = "calibration, geographic information system, geometry, image classification, radiometry, remote sensing, remotely piloted aircraft, spectroscopy, stereo vision",
    author = "Eija Honkavaara and Eskelinen, {Matti A.} and Ilkka P{\"o}l{\"o}nen and Heikki Saari and Harri Ojanen and Rami Mannila and Christer Holmlund and Teemu Hakala and Paula Litkey and tomi Rosnell and Niko Viljanen and Merja Pulkkanen",
    year = "2016",
    doi = "10.1109/TGRS.2016.2565471",
    language = "English",
    volume = "54",
    pages = "5440--5454",
    journal = "IEEE Transactions on Geoscience and Remote Sensing",
    issn = "0196-2892",
    publisher = "IEEE Institute of Electrical and Electronic Engineers",
    number = "9",

    }

    Remote sensing of 3-D geometry and surface moisture of a peat production area using hyperspectral frame cameras in visible to short-wave infrared spectral ranges onboard a small Unmanned Airborne Vehicle (UAV). / Honkavaara, Eija; Eskelinen, Matti A.; Pölönen, Ilkka; Saari, Heikki; Ojanen, Harri; Mannila, Rami; Holmlund, Christer; Hakala, Teemu; Litkey, Paula; Rosnell, tomi; Viljanen, Niko; Pulkkanen, Merja.

    In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, No. 9, 2016, p. 5440-5454.

    Research output: Contribution to journalArticleScientificpeer-review

    TY - JOUR

    T1 - Remote sensing of 3-D geometry and surface moisture of a peat production area using hyperspectral frame cameras in visible to short-wave infrared spectral ranges onboard a small Unmanned Airborne Vehicle (UAV)

    AU - Honkavaara, Eija

    AU - Eskelinen, Matti A.

    AU - Pölönen, Ilkka

    AU - Saari, Heikki

    AU - Ojanen, Harri

    AU - Mannila, Rami

    AU - Holmlund, Christer

    AU - Hakala, Teemu

    AU - Litkey, Paula

    AU - Rosnell, tomi

    AU - Viljanen, Niko

    AU - Pulkkanen, Merja

    PY - 2016

    Y1 - 2016

    N2 - Miniaturized hyperspectral imaging sensors are becoming available to small unmanned airborne vehicle (UAV) platforms. Imaging concepts based on frame format offer an attractive alternative to conventional hyperspectral pushbroom scanners because they enable enhanced processing and interpretation potential by allowing for acquisition of the 3-D geometry of the object and multiple object views together with the hyperspectral reflectance signatures. The objective of this investigation was to study the performance of novel visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral frame cameras based on a tunable Fabry-Pérot interferometer (FPI) in measuring a 3-D digital surface model and the surface moisture of a peat production area. UAV image blocks were captured with ground sample distances (GSDs) of 15, 9.5, and 2.5 cm with the SWIR, VNIR, and consumer RGB cameras, respectively. Georeferencing showed consistent behavior, with accuracy levels better than GSD for the FPI cameras. The best accuracy in moisture estimation was obtained when using the reflectance difference of the SWIR band at 1246 nm and of the VNIR band at 859 nm, which gave a root mean square error (rmse) of 5.21 pp (pp is the mass fraction in percentage points) and a normalized rmse of 7.61%. The results are encouraging, indicating that UAV-based remote sensing could significantly improve the efficiency and environmental safety aspects of peat production.

    AB - Miniaturized hyperspectral imaging sensors are becoming available to small unmanned airborne vehicle (UAV) platforms. Imaging concepts based on frame format offer an attractive alternative to conventional hyperspectral pushbroom scanners because they enable enhanced processing and interpretation potential by allowing for acquisition of the 3-D geometry of the object and multiple object views together with the hyperspectral reflectance signatures. The objective of this investigation was to study the performance of novel visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral frame cameras based on a tunable Fabry-Pérot interferometer (FPI) in measuring a 3-D digital surface model and the surface moisture of a peat production area. UAV image blocks were captured with ground sample distances (GSDs) of 15, 9.5, and 2.5 cm with the SWIR, VNIR, and consumer RGB cameras, respectively. Georeferencing showed consistent behavior, with accuracy levels better than GSD for the FPI cameras. The best accuracy in moisture estimation was obtained when using the reflectance difference of the SWIR band at 1246 nm and of the VNIR band at 859 nm, which gave a root mean square error (rmse) of 5.21 pp (pp is the mass fraction in percentage points) and a normalized rmse of 7.61%. The results are encouraging, indicating that UAV-based remote sensing could significantly improve the efficiency and environmental safety aspects of peat production.

    KW - calibration

    KW - geographic information system

    KW - geometry

    KW - image classification

    KW - radiometry

    KW - remote sensing

    KW - remotely piloted aircraft

    KW - spectroscopy

    KW - stereo vision

    U2 - 10.1109/TGRS.2016.2565471

    DO - 10.1109/TGRS.2016.2565471

    M3 - Article

    VL - 54

    SP - 5440

    EP - 5454

    JO - IEEE Transactions on Geoscience and Remote Sensing

    JF - IEEE Transactions on Geoscience and Remote Sensing

    SN - 0196-2892

    IS - 9

    ER -