Weighted Emissivity Model for Thermal Mapping Accuracy
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Solution Overview
Problem
Current thermal remote sensing technologies, particularly microbolometer sensors, face challenges in accurately estimating thermal emissivity due to differences in spectral characteristics compared to satellite-based systems, leading to inaccuracies in kinematic temperature mapping, especially for portable and miniaturized sensors.
Innovation Solution
A method is developed to generate a weighted emissivity model using spectral emissivity data from airborne imaging spectrometers and satellite optical imaging information, which is then applied to thermal imaging data to create a more accurate kinematic thermal map, addressing the spectral response discrepancies and enhancing spatial emissivity estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If microbolometer sensors are used for thermal remote sensing, then portability and miniaturization are improved, but measurement precision of thermal emissivity deteriorates due to spectral characteristic differences
Solution Approach 1:
The patent introduces an emissivity correction module as an intermediary component that processes the raw thermal data from microbolometer sensors. This module applies spectral response correction algorithms to compensate for the inherent spectral characteristic differences between microbolometer sensors and reference sensors, thereby improving measurement precision without sacrificing portability.
Solution Approach 2:
The patent modifies the operational parameters of the microbolometer sensor by applying spectral response correction factors and emissivity adjustment parameters. These parameter changes enable the sensor to account for its specific spectral characteristics and produce accurate thermal emissivity measurements despite the inherent limitations of miniaturized sensors.
2Measurement precision
If spectral response correction is applied to microbolometer data, then kinematic temperature mapping accuracy is improved, but device complexity increases
Solution Approach 1:
The patent performs spectral response correction and emissivity calibration as preliminary actions during the data acquisition phase. By pre-processing the thermal data with correction algorithms before final analysis, the system achieves accurate kinematic temperature mapping without requiring complex real-time processing, thus managing device complexity effectively.
Solution Approach 2:
The patent creates a corrected version of the thermal data by applying spectral response correction algorithms that replicate the performance of more complex reference sensors. This copying approach allows microbolometer sensors to produce accurate results through software-based corrections rather than requiring hardware complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables precise thermal emissivity mapping, improving the accuracy of kinematic temperature estimation and surface temperature measurements, particularly beneficial for agricultural and environmental monitoring applications.
Implementation Method 1
spectral emissivity data for the geographic area, which may be obtained using an airborne thermal imaging spectrometer
Implementation Method 2
thermal imaging data for the geographic area, which can be obtained using an airborne thermal imaging sensor
Implementation Method 3
use the emissivity values in the emissivity model to estimate kinematic thermal imaging data
Data Source
AI summary
A technology is described for spatially estimating thermal emissivity. A method can include obtaining spectral emissivity data and satellite imaging data for a geographic area. A weighted emissivity model of emissivity values may be generated for surfaces included in the geographic area from the spectral emissivity data and the satellite imaging data, wherein the spectral emissivity data is mapped to the satellite imaging data to generate the weighted emissivity model. Thermal imaging data for the geographic area may be received from an airborne thermal imaging sensor and a thermal emissivity map can be generated for the geographic area using the thermal imaging data and the weighted emissivity model. The emissivity values from the weighted emissivity model can be used to estimate thermal emissivity values.