radiometric calibration of digital images

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


Why is calibration necessary?Geometry: Lens Distortions


Why is calibration necessary?Radiometric Calibration

◦Conversion of recorded DN values (0-255) to at-surface reflectance (%)


◦Compare datasets across sensors, days, and locations with quantifiable unitsDN x Calibration Coefficient = Reflectance (%)


◦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)


◦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


spectrometer

Not this Jaz!


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


2) Spectrometer measures reflectance of reference targets


3) Camera captures reference target in several images while in flight


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

https://publiclab.org/notes/nedhorning/05-01-2014/improved-diy-nir-camera-calibration

http://www.kartverket.no/globalassets/kart/flyfoto/state-of-the-art-within-radiometric-correction-of-large-format-aerial-photogrammetric-images.pdf

http://www.kartverket.no/globalassets/kart/flyfoto/state-of-the-art-within-radiometric-correction-of-large-format-aerial-photogrammetric-images.pdf

radiometric calibration of digital images

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