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

32 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

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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",
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language = "English",
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pages = "5440--5454",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
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publisher = "Institute of Electrical and Electronic Engineers IEEE",
number = "9",

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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

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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

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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

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DO - 10.1109/TGRS.2016.2565471

M3 - Article

VL - 54

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EP - 5454

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

SN - 0196-2892

IS - 9

ER -