A Novel Tilt Correction Technique for Irradiance Sensors and Spectrometers On-Board Unmanned Aerial Vehicles

In unstable atmospheric conditions, using on-board irradiance sensors is one of the only robust methods to convert unmanned aerial vehicle (UAV)-based optical remote sensing data to reflectance factors. Normally, such sensors experience significant errors due to tilting of the UAV, if not installed on a stabilizing gimbal. Unfortunately, such gimbals of sufficient accuracy are heavy, cumbersome, and cannot be installed on all UAV platforms. In this paper, we present the FGI Aerial Image Reference System (FGI AIRS) developed at the Finnish Geospatial Research Institute (FGI) and a novel method for optical and mathematical tilt correction of the irradiance measurements. The FGI AIRS is a sensor unit for UAVs that provides the irradiance spectrum, Real Time Kinematic (RTK)/Post Processed Kinematic (PPK) GNSS position, and orientation for the attached cameras. The FGI AIRS processes the reference data in real time for each acquired image and can send it to an on-board or on-cloud processing unit. The novel correction method is based on three RGB photodiodes that are tilted 10◦ in opposite directions. These photodiodes sample the irradiance readings at different sensor tilts, from which reading of a virtual horizontal irradiance sensor is calculated. The FGI AIRS was tested, and the method was shown to allow on-board measurement of irradiance at an accuracy better than ±0.8% at UAV tilts up to 10◦ and ±1.2% at tilts up to 15◦. In addition, the accuracy of FGI AIRS to produce reflectance-factor-calibrated aerial images was compared against the traditional methods. In the unstable weather conditions of the experiment, both the FGI AIRS and the on-ground spectrometer were able to produce radiometrically accurate and visually pleasing orthomosaics, while the reflectance reference panels and the on-board irradiance sensor without stabilization or tilt correction both failed to do so. The authors recommend the implementation of the proposed tilt correction method in all future UAV irradiance sensors if they are not to be installed on a gimbal.

[1]  C. Long,et al.  A Method of Correcting for Tilt from Horizontal in Downwelling Shortwave Irradiance Measurements on Moving Platforms , 2010 .

[2]  Eija Honkavaara,et al.  Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features , 2018, Remote. Sens..

[3]  Jan G. P. W. Clevers,et al.  Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data - potential of unmanned aerial vehicle imagery , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[4]  Huanhuan Yuan,et al.  The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager , 2017, Remote. Sens..

[5]  Eija Honkavaara,et al.  Radiometric block adjustment of hyperspectral image blocks in the Brazilian environment , 2018 .

[6]  K. Nurminen,et al.  A Permanent Test Field for Digital Photogrammetric Systems , 2008 .

[7]  Pablo J. Zarco-Tejada,et al.  Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Juha Suomalainen,et al.  The selection of appropriate spectrally bright pseudo-invariant ground targets for use in empirical line calibration of SPOT satellite imagery , 2011 .

[9]  E. Honkavaara,et al.  SPECTRAL IMAGING FROM UAVS UNDER VARYING ILLUMINATION CONDITIONS , 2013 .

[10]  Arko Lucieer,et al.  HyperUAS—Imaging Spectroscopy from a Multirotor Unmanned Aircraft System , 2014, J. Field Robotics.

[11]  Arko Lucieer,et al.  Comparison of MEMS-based and FOG-based IMUs to determine sensor pose on an unmanned aircraft system , 2017 .

[12]  E. Honkavaara,et al.  Bidirectional reflectance spectrometry of gravel at the Sjökulla test field , 2007 .

[13]  Diofantos G. Hadjimitsis,et al.  Precipitation effects on the selection of suitable non-variant targets intended for atmospheric correction of satellite remotely sensed imagery , 2013 .

[14]  Sindy Sterckx,et al.  Atmospheric correction of APEX hyperspectral data , 2016 .

[15]  E. Honkavaara,et al.  Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables , 2017 .

[16]  Arve Kylling,et al.  Unmanned aerial system nadir reflectance and MODIS nadir BRDF-adjusted surface reflectances intercompared over Greenland , 2016 .

[17]  B. T. San,et al.  EVALUATION OF DIFFERENT ATMOSPHERIC CORRECTION ALGORITHMS FOR EO-1 HYPERION IMAGERY , 2010 .

[18]  Eija Honkavaara,et al.  Point Cloud Generation from Aerial Image Data Acquired by a Quadrocopter Type Micro Unmanned Aerial Vehicle and a Digital Still Camera , 2012, Sensors.

[19]  P. Zarco-Tejada,et al.  Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera , 2012 .

[20]  Cyrill Stachniss,et al.  Fast and effective online pose estimation and mapping for UAVs , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[21]  A. Burkart,et al.  A Novel UAV-Based Ultra-Light Weight Spectrometer for Field Spectroscopy , 2014, IEEE Sensors Journal.

[22]  C. Gueymard Parameterized transmittance model for direct beam and circumsolar spectral irradiance , 2001 .

[23]  Eija Honkavaara,et al.  Direct Reflectance Measurements from Drones: Sensor Absolute Radiometric Calibration and System Tests for Forest Reflectance Characterization , 2018, Sensors.

[24]  Eija Honkavaara,et al.  OPTIMIZING RADIOMETRIC PROCESSING AND FEATURE EXTRACTION OF DRONE BASED HYPERSPECTRAL FRAME FORMAT IMAGERY FOR ESTIMATION OF YIELD QUANTITY AND QUALITY OF A GRASS SWARD , 2018 .

[25]  Teemu Hakala,et al.  Measurement of Reflectance Properties of Asphalt Surfaces and Their Usability as Reference Targets for Aerial Photos , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[26]  T. Painter,et al.  Reflectance quantities in optical remote sensing - definitions and case studies , 2006 .

[27]  Juha Hyyppä,et al.  Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging , 2017, Remote. Sens..

[28]  Juha Suomalainen,et al.  A comparison of methods for the retrieval of surface reflectance factor from multitemporal SPOT HRV, HRVIR, and HRG multispectral satellite imagery , 2010 .

[29]  Christian Eling,et al.  Real-Time Single-Frequency GPS/MEMS-IMU Attitude Determination of Lightweight UAVs , 2015, Sensors.

[30]  Eija Honkavaara,et al.  Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows , 2018, Remote. Sens..

[31]  Eija Honkavaara,et al.  Radiometric Correction of Close-Range Spectral Image Blocks Captured Using an Unmanned Aerial Vehicle with a Radiometric Block Adjustment , 2018, Remote. Sens..

[32]  E. Honkavaara,et al.  Radiometric Calibration and Characterization of Large-format Digital Photogrammetric Sensors in a Test Field , 2008 .

[33]  E. Milton,et al.  The use of the empirical line method to calibrate remotely sensed data to reflectance , 1999 .

[34]  Heikki Saari,et al.  Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture , 2013, Remote. Sens..

[35]  Anthony W. Sarto,et al.  The Stabilized Radiometer Platform (STRAP)—An Actively Stabilized Horizontally Level Platform for Improved Aircraft Irradiance Measurements , 2008 .

[36]  Serge Bories,et al.  Low-cost Real-time Tightly-Coupled GNSS/INS Navigation System Based on Carrier-phase Double- differences for UAV Applications , 2014 .