Remote Material Identification Performance Prediction Tool
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Solution Overview
Problem
Existing material identification processes using remote sensors face challenges in accurately predicting performance under real-world conditions and uncertainties, such as varying environmental factors and sensor calibration, especially when dealing with non-Lambertian materials.
Innovation Solution
A computer-implemented system and method that transforms measured reflectance values and environmental uncertainties into performance predictions for remote material identification, using directional hemispherical reflectance and bi-directional reflectance distribution functions to predict spectral reflectance of candidate materials, allowing for the selection and setup of material identification systems and interpretation of imagery results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If remote sensors are used to detect and identify materials, then material identification capability is improved, but measurement precision deteriorates due to environmental factors and sensor calibration uncertainties
Solution Approach 1:
The system performs preliminary atmospheric correction and calibration computations before material identification. It pre-calculates atmospheric transmission functions, adjusts for sensor calibration uncertainties, and prepares corrected reflectance values in advance, allowing the actual material identification to proceed with already-compensated measurements that account for environmental factors.
Solution Approach 2:
The system incorporates feedback mechanisms by iteratively comparing identified materials against known spectral signatures and adjusting classifications based on confidence levels. It uses probability of detection and false alarm rate calculations to feedback-correct identification results, continuously refining accuracy based on performance metrics and environmental condition data.
2Measurement precision
If environmental conditions and sensor uncertainties are accounted for, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system uses a universal atmospheric correction module that handles multiple environmental factors (aerosol optical depth, water vapor, temperature, pressure) through a single integrated correction function. It employs a multi-functional calibration framework that simultaneously corrects for sensor radiometric calibration, spectral calibration, and environmental variations, reducing overall system complexity by consolidating multiple correction operations into unified algorithms.
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
The system effectively predicts the performance of remote material identification processes, reducing errors in material classification by accounting for environmental conditions and sensor uncertainties, thereby improving the accuracy of material identification.
Implementation Method 1
The spectral radiance—radiance at a given wavelength or band—for any given target in a scene will depend on the material of which the target is composed (the 'target material'), as well as the spectrum and angle of irradiation being reflected by the target
Implementation Method 2
The bi-directional reflectance distribution function (BRDF) for each candidate material, which relates, for a given wavelength or band of wavelengths and direction of incident irradiation, reflected radiance in the direction of the sensor
Data Source
AI summary
In accordance with the present disclosure, a computer implemented system and method predicts the performance for a remote material identification process under real conditions and uncertainties. The method and system transforms data representing measured reflectance values for candidate materials based on environmental conditions, and uncertainties regarding the environmental conditions and/or calibration of sensors measuring radiance values into the performance predictions for a material identification process operating under those conditions and uncertainties. The performance predictions can be communicated to a designer of, for example, a multi-angle material identification system for use in selecting and setting up the system, or communicated to a consumer of images captured by the material identification system for use in interpreting results of application of the material identification process to real imagery acquired with remote sensors.