Temperature and Emissivity Separation ‘Draping’ Algorithm Applied to Hyperspectral Infrared Data
Abstract
:1. Introduction
- the surface is not a blackbody, thus emissivity contribution is not negligible;
- part of the radiation emitted by the surface is absorbed, reflected and scattered by the atmosphere.
2. Materials and Methods
2.1. Experimental Data Acquisition
2.2. Field of View and Data Saturation
2.3. Atmospheric Corrections
2.4. Normalised Emissivity Method Applied to Molten Lava
2.5. The “Draping” Algorithm
- Identify the largest radiance Rmax over all available wavelengths.
- Create a lookup table of spectra derived from Equation (8) by varying Th, Tc and fh. Only spectra respecting the condition R(λ, Ti) ≥ Rmax may be considered for comparison with measured radiances. This constraint guarantees that ε(λ) ≤ 1.
- Compute cross-correlation between radiance measurements and each spectrum of the lookup table and calculate the Spearman rank correlation coefficient.
- Identify the maximal Spearman rank correlation (Ti,,λ), which best matches the measured radiances and therefore identifies the triplet of values Th, Tc and fh fitting the thermal two-components model.
3. Results
4. Discussion
4.1. MethodValidation Using the Lava Simulator
- the temperature of the alloy is independent of the distance;
- the fraction of the alloy within the camera’s FOV (given as an angle in the product specification) is estimated based on the radius of the alloy and the distance to the camera;
- the temperature of the background is estimated from the pixels observing the plywood close to the alloy;
- two thermal observations in different spectral regions are available.
- the emissivity step in the iterative solution is 0.05;
- we only know the optical SWIR filter response and not the whole optics–sensor–electronics response;
- the SWIR filter already covers a water absorption band (see the noise in Figure 6 at 2.5µm).
4.2. Sub-Pixel Temperatures
- the first phase, in which the basalt is completely melted;
- the second phase, in which two thermal components are identified;
- the third phase, which corresponds to the complete solidification of the basalt.
4.3. Methodological Innovation Compared to Previous Methods for Temperature/Emissivity Separation
- parameters for atmospheric correction;
- more than a single temperature of the target at the sensor pixel scale (sub-pixel temperatures).
- an accurate atmospheric correction of the data;
- the availability of high-spectral resolution(hyperspectral) data.
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Setup | Foreoptic | Filter | Target Area Diameter | Max Unsaturated Temperature | Acquired Number of spectra |
---|---|---|---|---|---|
#1 | Opt1 | F2 | 1.4 cm | ~1100 °C | 40 |
#2 | Opt1 | F5 | 1.4 cm | ~1300 °C | 210 |
#3 | Opt2 | F2 | 4.0 cm | ~1100 °C | 50 |
#4 | Opt2 | F5 | 4.0 cm | ~1300 °C | 220 |
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Lombardo, V.; Pick, L.; Spinetti, C.; Tadeucci, J.; Zakšek, K. Temperature and Emissivity Separation ‘Draping’ Algorithm Applied to Hyperspectral Infrared Data. Remote Sens. 2020, 12, 2046. https://doi.org/10.3390/rs12122046
Lombardo V, Pick L, Spinetti C, Tadeucci J, Zakšek K. Temperature and Emissivity Separation ‘Draping’ Algorithm Applied to Hyperspectral Infrared Data. Remote Sensing. 2020; 12(12):2046. https://doi.org/10.3390/rs12122046
Chicago/Turabian StyleLombardo, Valerio, Leonie Pick, Claudia Spinetti, Jacopo Tadeucci, and Klemen Zakšek. 2020. "Temperature and Emissivity Separation ‘Draping’ Algorithm Applied to Hyperspectral Infrared Data" Remote Sensing 12, no. 12: 2046. https://doi.org/10.3390/rs12122046