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Radiometric Calibration of Digital Images
Capstone Project bySean Thibert
in conjunction with AGRG
Overview
• Project Goals
• Background Information
• Materials
• First Test (Linear)
• Python Script
• Second Test (Exponential)
• GNDVI Results
• Next Steps
• Conclusion
Evaluate current methods for radiometric calibration of a converted “off-the-shelf” digital camera.
Develop a time and cost effective solution to integrate into AGRG’s UAV data collection and processing workflow.
Goals
Why is calibration necessary?
Satellite Sensors vs. Digital Cameras
Landsat Operational Land Imager (OLI) Nikon 1 J4 Digital Camera
• Highly specialized pieces of scientific equipment
• Compensate for 705km worth of atmosphere
• Have been operational for decades• Refined laboratory-based
calibration methods
• Off-the-shelf camera modified to allow for Near Infra-Red (NIR) light to be captured
• A relatively new mapping tool in conjunction with Unmanned Aerial Vehicles (UAV)
• Mission altitude < 90m, negligible atmospheric effects
• Unique calibration requirements per camera• However, for all the same
reasons…
Why is calibration necessary?Noise: Erroneous sensor measurements
Kelcey & Lucieer, 2012
Why is calibration necessary?Vignetting: Pixel brightness changes radially away from principal point as a factor of aperture
Kelcey & Lucieer, 2012
Why is calibration necessary?Geometry: Lens Distortions
Kelcey & Lucieer, 2012
Why is calibration necessary?Radiometric Calibration
◦Conversion of recorded DN values (0-255) to at-surface reflectance (%)
Why is calibration necessary?Radiometric Calibration
◦Compare datasets across sensors, days, and locations with quantifiable unitsDN x Calibration Coefficient = Reflectance (%)
Why is calibration necessary?Radiometric Calibration
◦Compare datasets across sensors, days, and locations with quantifiable units
◦Normalize images across one survey with solar irradiance values
DN x Calibration Coefficient = Reflectance (%)
Reflectance (%) x Solar Irradiance (kW/m2)
Why is calibration necessary?Radiometric Calibration
◦Band-specific calibrations improve accuracy of vegetation indices
◦Focus of this project:
Materials Nikon 1 J4 converted digital
camera system for PrecisionHawk Lancaster UAV
Materials Dataset of 29 images from
Mosher’s Corners, NS◦ Acquired September 18, 2015
*all calibration images mimicked the above, except for altitude
MaterialsOcean Optics Inc. JAZ portable
spectrometer
MaterialsOcean Optics Inc. JAZ portable
spectrometer
Not this Jaz!
MaterialsOcean Optics Inc. JAZ portable
spectrometer• Measures spectral information from 350 – 1000 nm
• Adjusted for solar irradiance with a Labsphere Spectralon Reference Panel (95% Reflectance)
• SpectraSuite software for visualizing and saving reflectance data
MaterialsMulticolour and Grayscale
reference targets
5% 20% 30% 40% 50% 60% 70%
80%
90%
Software
Fiji (Open source image analysis package)
Open source distribution of Python. Used GDAL, SciPy, Numpy, and MatlibPlot libraries
Photogrammetry software used for mosaicking
ArcMap 10.3 used for GNDVIproducts
Where do they all add in?
1) Spectralon panel reflects 95% of incoming sunlight,calibrating the spectrometer
Where do they all add in?
2) Spectrometer measures reflectance of reference targets
Where do they all add in?
3) Camera captures reference target in several images while in flight
Where do they all add in?
4) DN values plotted against “true” reflectance values to determine relationship
Dependent variable
Independent variable
Linear Calibration ModelNed Horning Public Lab post
◦ Straight-forward methods◦ Inexpensive materials
Used multicolour reference target instead◦ Assumed linear relationship between DN
and reflectance
Results
Data for each camera band and target
Python ScriptInput CSV with band-specific target reflectance information and corresponding DN values
Script determines optimal regression equation for the data, and stores it for use
User provides input and output location for images to be calibrated
Script converts input image to 2D array, and applies calibration equation to DN values for each band.
Writes new reflectance values to output .TIF image.
Results
0 50 100 150 200 2500
10
20
30
40
50
60
70
80
90
f(x) = 0.503157643848765 x − 37.7728318490778R² = 0.781190355365844
NIR Linear (NIR)
DN
Refle
ctan
ce (
%)
Linear regression of NIR band• Similar relationship with Green and Blue• R2 = 0.7812
Results
0 50 100 150 200 2500
10
20
30
40
50
60
70
80
90
f(x) = 0.503157643848765 x − 37.7728318490778R² = 0.781190355365844
NIR Linear (NIR)
DN
Refle
ctan
ce (
%)
X-intercept causing negative reflectance values
Results
0 50 100 150 200 2500
10
20
30
40
50
60
70
80
90f(x) = 1.34693958102386 exp( 0.0201328562615177 x )R² = 0.900567680735934
NIR
Exponential (NIR)
DN
Refle
ctan
ce (
%)
Exponential regression is a better fit! • Removes negative values• R2 = 0.90057
Empirical Line Calibration ModelMethodology from Wang et al.
(2015) “A simplified empirical line calibration method for sUAS-Based
Remote Sensing”, ASPRS
Noticed similar exponential relationship between DN and reflectance
Used grayscale reference target instead of coloured
More rigorous approach to data collection
Empirical Line Calibration ModelAdvocate calibrating each band
separately
Performs a negative natural log transformation on exponential relationships to linearize the model
Transforms the values back to reflectance for calibration
Results
Data for each camera band and grayscale target
ResultsRegression equations applied to each band from grayscale target panels
NIR Green
Blue
ResultsNegative natural log-transformed data for NIR and Blue bands, due to their use of an exponential regression curve
Final Calibration EquationsNIR:
Green:
Blue:
Applied to all images within the Mosher’s Corners dataset
GNDVI Results
1: GNDVI of 0.23 (indicative of vegetation)2: GNDVI of 0.28 (indicative of vegetation)
1
2
1
2
1: GNDVI of -0.14 2: GNDVI of -0.03 (indicative of dirt)
Uncalibrated Calibrated
GNDVI Results
1: GNDVI of 0.072: GNDVI of 0.60 (indicative of healthy vegetation)
1
2
1
2
1: GNDVI of -0.0612: GNDVI of 3.38 (indicative of shadows)
Note: Over and under exposed DN values causing shadows
Uncalibrated Calibrated
GNDVI Results
1: GNDVI of 0.27 (indicative of vegetation)2: GNDVI of 0.23 (indicative of vegetation)
2
1
2
1
1: GNDVI of 0.04 2: GNDVI of -0.08 (indicative of dirt)
Next StepsBetter calibration targetImages recorded in RAW formatIn-situ testingImprove Python script:
◦Batch process◦GUI◦Apply solar irradiance values
ConclusionRelationship between DN and
reflectance is exponential, not linear
Empirical Line Method shows promise
Most of the workflow can (and will) be automated
References• Berra, E., S. Gibson-Poole, A. MacArthur, R. Gaulton, A. Hamilton. “Estimation of the spectral sensitivity functions of
un-modified and modified commercial off-the-shelf digital cameras to enable their use as a multispectral imaging system for UAVs”. Remote Sensing and Spatial Information Sciences, Volume XL-1/W4. Presented at the International Conference on Unmanned Aerial Vehicles in Geomatics (2015)
• Haest, B., J. Biesemans, W. Horsten, J. Everaerts, N. Van Camp, J. Van Valckenborgh. “Radiometric Calibration of
Digital Photogrammetric Camera Image Data”. ASPRS 2009 Annual Conference, Baltimore, Maryland (2009) • Horning, N. “Improved DIY NIR camera calibration”, PublicLab.org (2014). Accessed online at:
https://publiclab.org/notes/nedhorning/05-01-2014/improved-diy-nir-camera-calibration • Kelcey, J. and A. Lucieer. “Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing”.
Remote Sensing, Volume 4, Issue 5, pg 1462-1493 (2012) • Laliberte, A., M. Goforth, C. Steele, A. Rango. “Multispectral Remote Sensing from Unmanned Aircraft: Image
Processing Workflows and Applications for Rangeland Environments”. Remote Sensing, Volume 3, pg 2529-2551 (2011)
• Lelong, C., P. Burger, G. Jubelin, B. Roux, S. Labbe, F. Baret. “Assessment of Unmanned Aerial Vehicles Imagery for
Quantitative Monitoring of Wheat Crop in Small Plots”. Sensors, Volume 8, Issue 5, pg 3557-3585 (2008) • Ryan, R. and M. Pagnutti. “Enhanced Absolute and Relative Radiometric Calibration for Digital Aerial Cameras”.
Photogrammetric Week ’09, pg 81-90 (2009) • Von Bueren, S. and I. Yule. “Multispectral Aerial Imaging of Pasture Quality and Biomass using Unmanned Aerial
Vehicles (UAV)”. New Zealand Centre for Precision Agriculture, Institue of Agriculture and Environment, Massey University (2013)
• Wang, C. & S. Myint. “A Simplified Empirical Line Method of Radiometric Calibration for Small Unmanned Aircraft
Systems-Based Remote Sensing”. Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5) (2015).
• For more visual and technical information, please visit the following link from the Finnish Geodetic Institute: http://www.kartverket.no/globalassets/kart/flyfoto/state-of-the-art-within-radiometric-correction-of-large-format-aerial-photogrammetric-images.pdf