Systems and methods for differentiable programming for hyperspectral unmixing
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Solution Overview
Problem
Existing hyperspectral imaging technologies face challenges in accurately quantifying material presence in mixtures due to inherent spectral variability of endmembers, leading to significant errors in unmixing algorithms, particularly when dealing with subtle absorption band differences caused by factors such as different grain sizes or varying molecular bond ratios.
Innovation Solution
A system incorporating a generative dispersion model into an end-to-end spectral unmixing pipeline using differentiable programming to simulate spectral variability, allowing for analysis-by-synthesis optimization and iteratively optimizing dispersion model parameters to identify material abundances, with optional use of a convolutional neural network for inverse rendering when training data is available.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional hyperspectral imaging with fixed spectral bands is used, then spectral resolution is maintained, but measurement precision deteriorates due to spectral variability of endmembers
Solution Approach 1:
The patent applies dynamics by making the spectral unmixing process adaptive through iterative optimization. The system dynamically adjusts the unmixing parameters and spectral signatures during the optimization process to account for spectral variability, rather than using fixed signatures. This allows the system to adapt to different material compositions and spectral conditions, improving measurement precision despite spectral variability.
Solution Approach 2:
The patent changes parameters by optimizing spectral signatures and abundance parameters iteratively. Instead of using fixed spectral libraries, the system allows spectral parameters to be adjusted and refined during the unmixing process based on the observed hyperspectral data, thereby compensating for spectral variability and improving unmixing accuracy.
2Measurement precision
If spectral resolution is increased beyond traditional RGB bands, then material recognition capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces complex physical spectral libraries and manual material identification methods with an automated optimization-based system. By using iterative optimization algorithms, the system substitutes the need for extensive pre-acquired spectral data and complex optical setups with a computational approach that achieves similar or better material recognition accuracy.
Solution Approach 2:
The system performs self-service by automatically optimizing its own spectral signatures and unmixing parameters without requiring external spectral libraries or manual calibration. The algorithm learns and adapts spectral characteristics directly from the hyperspectral data, reducing the need for external resources and simplifying the overall system requirements.
3Measurement precision
If iterative optimization is performed to account for spectral variability, then unmixing accuracy is improved, but processing time increases
Solution Approach 1:
The patent maintains continuity of useful action by implementing an iterative optimization process that continuously refines the unmixing results. Each iteration builds upon the previous one, progressively improving abundance estimation accuracy. The continuous refinement process ensures that computational effort is efficiently utilized to achieve convergence, balancing accuracy improvement with processing time.
Data Source
AI summary
Various embodiments of a system for linear unmixing of spectral images using a dispersion model are disclosed herein.