Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing
"> Graphical abstract
">
<p>Comparative imagery of saltmarsh captured at different scales with different platforms: satellite, UAV, field (satellite imagery: GoogleEarth).</p> ">
<p>Modified Tetracam Miniature Multiple Camera Array (Mini-MCA).</p> ">
<p>Image data pre-processing: Sensor correction and radiometric calibration.</p> ">
<p>Illustration of the effects of increased noise proportion: Original image, 5 % noise, 25 % noise</p> ">
<p>Relative Monochromatic Response and Absolute Filter Transmission.</p> ">
<p>Illustration of the effects of vignetting: Original image, image exhibiting the radial shadowing of vignetting.</p> ">
<p>Forms of lens distortion: original, barrel lens distortion, pincushion lens distortion.</p> ">
<p>Dark offset imagery from the six channels of the mini-MCA: single sample, average of 125 samples, standard deviation of 125 samples.</p> ">
<p>Dark offset imagery from the six channels of the mini-MCA: single sample, average of 125 samples, standard deviation of 125 samples.</p> ">
<p>Distribution of noise within dark offset imagery for all six channels of the mini-MCA (Exposure 1,000 μs).</p> ">
Abstract
:1. Introduction
- identification, assessment and quantification of the components of data modification within a consumer level multispectral sensor;
- implementation of image-based radiometric correction techniques; and
- assessment of post-radiometric correction data quality issues.
1.1. UAV Multispectral Sensors
2. Methods
2.1. Noise Correction
2.1.1. Dark Offset Subtraction
2.1.2. Dark Offset Image Generation Methodology
2.2. Radiance Strength Modification
2.2.1. Monochromatic Response
2.2.2. Filter Transmittance
2.3. Wavelength Dependent Correction Factor Methodology
2.3.1. Flat Field Correction Factors
2.3.2. Vignetting Correction Methodology
2.4. Lens Distortion
2.4.1. Brown–Conrady Model
2.4.2. Lens Distortion Correction Methodology
2.5. Salt Marsh Case Study
3. Results
3.1. Dark Offset Subtraction
3.1.1. Global Checkered Pattern
3.1.2. Periodic Noise
3.1.3. Progressive Shutter Band Noise
3.2. Dark Offset Potential
3.3. Filter Transmission/Monochromatic Efficiency
3.4. Vignetting
3.4.1. Effect of Sensors
3.4.2. Effect of Exposure
3.4.3. Effect of Filters
3.5. Lens Distortion
Agisoft Lens Calibration Coefficients
3.6. Salt Marsh Case Study
4. Discussion
4.1. Channel Dual Distributions
4.2. Vignetting Model
4.3. Sensor Dynamic Range
4.4. UAV Sensor Selection
5. Conclusions
Acknowledgments
References
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Filter (nm) | Transmission (%) | Correction Factor | Monochromatic Relative Efficiency (%) | Correction Factor | Multiplicative Correction Factor |
---|---|---|---|---|---|
450 | 0.44 | 2.28 | 0.16 | 6.25 | 14.27 |
490 | 0.47 | 2.13 | 0.34 | 2.97 | 6.32 |
530 | 0.47 | 2.12 | 0.56 | 1.80 | 3.81 |
550 | 0.45 | 2.21 | 0.62 | 1.61 | 3.57 |
570 | 0.44 | 2.26 | 0.67 | 1.49 | 3.38 |
670 | 0.56 | 1.80 | 0.91 | 1.10 | 1.98 |
700 | 0.56 | 1.79 | 0.93 | 1.08 | 1.92 |
720 | 0.51 | 1.96 | 0.95 | 1.05 | 2.06 |
750 | 0.49 | 2.02 | 0.97 | 1.03 | 2.09 |
900 | 0.48 | 2.07 | 0.71 | 1.40 | 2.90 |
970 | 0.47 | 2.14 | 0.45 | 2.22 | 4.75 |
Date | Site | Longitude | Latitude | Height (m) | Exposure (μs) |
---|---|---|---|---|---|
25/11/2012 | Ralphs Bay | 42 55.742′S | 147 29.036′E | 100 m | 4,000 |
Channel | State | Average | StDev | Skew |
---|---|---|---|---|
1 | 1 | 8.445 | 0.650 | −3.379 |
2 | 8.452 | 0.6817 | −2.987 | |
2 | 1 | 1.828 | 0.884 | 1.559 |
2 | 15.972 | 0.670 | −23.798 | |
3 | 1 | 6.999 | 0.317 | −18.182 |
2 | 11.981 | 0.504 | −23.543 | |
4 | 1 | 9.374 | 0.626 | −5.627 |
2 | 14.974 | 0.628 | −23.801 | |
5 | 1 | 7.757 | 0.542 | −5.664 |
2 | 5.527 | 0.747 | −1.094 | |
M | 1 | 8.020 | 0.449 | −10.784 |
2 | 3.508 | 0.762 | 1.247 |
Channel | cx | cy | k1 | k2 | p1 | p2 | Fx | Fy |
---|---|---|---|---|---|---|---|---|
1 | 629.169 | 465.738 | −0.068745 | 0.0623006 | −0.000639335 | −0.000509879 | 1622.5 | 1622.5 |
2 | 628.961 | 464.003 | −0.0579649 | 0.0356426 | −0.000102067 | −0.00221439 | 1606.81 | 1606.81 |
3 | 632.575 | 472.777 | −0.0506697 | 0.021484 | 0.000077687 | 0.0011317 | 1625.74 | 1625.74 |
4 | 633.999 | 470.756 | −0.0912427 | 0.132531 | −0.000135051 | 0.00124068 | 1623.55 | 1623.55 |
5 | 632.498 | 470.568 | −0.0748613 | 0.0729301 | 0.000851022 | −0.000399902 | 1625.88 | 1625.88 |
M | 638.965 | 460.592 | −0.0922108 | 0.124107 | 0.000614466 | 0.000842289 | 1619.26 | 1619.26 |
Share and Cite
Kelcey, J.; Lucieer, A. Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing. Remote Sens. 2012, 4, 1462-1493. https://doi.org/10.3390/rs4051462
Kelcey J, Lucieer A. Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing. Remote Sensing. 2012; 4(5):1462-1493. https://doi.org/10.3390/rs4051462
Chicago/Turabian StyleKelcey, Joshua, and Arko Lucieer. 2012. "Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing" Remote Sensing 4, no. 5: 1462-1493. https://doi.org/10.3390/rs4051462
APA StyleKelcey, J., & Lucieer, A. (2012). Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing. Remote Sensing, 4(5), 1462-1493. https://doi.org/10.3390/rs4051462