Statistical Models for Atmospheric Correction in EOS Imagery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing Earth observation satellite (EOS) images are degraded by atmospheric effects such as aerosols and gases, which affect the utility of the imagery by altering reflectance and radiance, making it difficult to obtain accurate surface reflectance data.
Innovation Solution
A method and system for generating a statistical model to estimate and remove atmospheric effects by using a vegetation yardstick, such as continuous healthy canopy vegetation, to calibrate and convert top-of-atmosphere reflectance to surface reflectance using a conceptual model and multiple regression techniques, employing GIS for sampling and calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If Earth observation satellites orbit above the atmosphere to monitor vast regions, then coverage area is improved, but atmospheric effects from aerosols and gases degrade image quality and reflectance accuracy
Solution Approach 1:
The patent uses vegetation with stable reflectance properties as an intermediary reference to indirectly measure and correct atmospheric effects. By comparing observed vegetation reflectance against known stable reference values, the system derives atmospheric correction factors that can then be applied to other areas of interest in the image
Solution Approach 2:
The system establishes a feedback loop where vegetation reference values serve as a control mechanism. The measured vegetation reflectance provides feedback about current atmospheric conditions, which then feeds back into the correction algorithm to adjust the removal of atmospheric effects across the entire image, ensuring continuous adaptation to varying atmospheric states
2Measurement precision
If atmospheric effects are removed to improve reflectance accuracy, then measurement precision is improved, but processing complexity increases
Solution Approach 1:
The patent implements self-service by using naturally occurring vegetation with inherently stable reflectance properties as the reference standard. This eliminates the need for external calibration targets or complex a priori atmospheric models, as the system uses the Earth's own vegetation to calibrate and correct the atmospheric effects
Solution Approach 2:
The system changes the approach from direct atmospheric measurement to deriving atmospheric parameters indirectly through vegetation reflectance analysis. By transforming the problem from measuring atmospheric properties directly to measuring vegetation properties and inferring atmospheric conditions, the processing becomes more tractable while maintaining accuracy
Data Source
AI summary
Systems and methods for generating and using statistical models to mitigate atmospheric effects in images are described. In some embodiments, a statistical model may be generated by selecting a vegetation type that grows in continuous healthy canopies; identifying a vegetation reference value that is a stable reflectance property of the vegetation type; in a plurality of images, selecting one or more plots of the vegetation type and obtaining top-of-atmosphere reflectance for the plots; selecting discrete areas near the plots and obtaining top-of-atmosphere reflectance for the discrete areas; obtaining image statistics for the discrete areas; and generating a statistical model based on the acquired data.