Automated Stray Light Image Labeling for Space Optical Systems
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Solution Overview
Problem
The generation of large and costly training databases for supervised machine learning in stray light characterization of space-related optical systems requires extensive human intervention and computational resources, making it inefficient.
Innovation Solution
An automated method that uses unsupervised machine learning to identify and label clusters of light in simulated images, applying transformations to generate an augmented set of labeled images for training machine learning models, reducing the need for extensive human verification and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classical statistical data analysis with human verification is used to obtain training data, then the quality and accuracy of labeled images is improved, but the time, cost, and computational resources required increase significantly
Solution Approach 1:
The patent applies preliminary action by using unsupervised machine learning to automatically perform the initial clustering and labeling of training data before any human verification is needed. The system pre-processes simulated images to identify and label stray light clusters, creating a ready-to-use training dataset that significantly reduces the time and resources required for subsequent human verification steps.
2Reliability
If extensive human intervention is used to segment and label training images, then the quality of training data is improved, but the cost and complexity of the process increase
Solution Approach 1:
The patent implements self-service by enabling the machine learning system to automatically perform data labeling without requiring extensive human intervention. The unsupervised learning algorithm autonomously segments and labels training images by identifying stray light clusters, making the system self-sufficient in generating high-quality training data while minimizing human involvement and associated complexity.
3Quantity of substance
If large training databases are generated through conventional methods, then the completeness of training data is improved, but the computational resources and expense increase
Solution Approach 1:
The patent applies copying by using transformations to generate multiple variations of labeled training images from a single set of manually labeled images. The system creates augmented training datasets by applying geometric transformations (rotation, flipping, scaling) to existing labeled images, thereby multiplying the effective training data volume without requiring proportional increases in computational resources for data generation.
Data Source
AI summary
A method for generating a labeled set of images for use in machine learning based stray light characterization for space-related optical systems. The method includes (a) obtaining a set of images simulated for a space-related optical system, wherein the images of the set of images contain stray light simulated for the space-related optical system, (b) for each image of the set of images, identifying one or more clusters of light contained in the respective image and labeling the respective image by the one or more clusters of light, wherein the one or more clusters of light include at least one cluster of stray light, and (c) creating, based on the labeled images of the set of images, a plurality of new labeled images by applying transformations to the labeled images to generate an augmented set of labeled images.