Synthetic Template Classification for Space Object Streak Detection
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Solution Overview
Problem
Detecting and classifying resident space objects (RSOs) in low Earth orbit using neural network-based classifiers is challenging due to the lack of curated and labeled training data, and the irregular shapes and small sizes of RSOs, which make it difficult to develop a trained dataset.
Innovation Solution
A system and method that generates a synthetic ground-truthed dataset using templates constructed from a statistical mean of shapes, allowing classification of streak patterns in images as either RSOs or stars without relying on real ground-truthed data, utilizing a detection module to identify streak patterns and a classifier module to compare them with templates in a synthetic dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural network-based classifiers are used to detect and classify RSOs, then classification accuracy can be improved, but the lack of curated and labeled training data worsens the ability to train these models
Solution Approach 1:
The patent creates synthetic copies of RSO images through template matching and image generation techniques. Templates representing different RSO types are used to generate realistic training images with known ground truth labels, effectively copying the essential characteristics of real RSOs without requiring actual labeled photographs of space objects.
Solution Approach 2:
The patent extracts key features and characteristics of RSOs by creating simplified templates that represent essential object properties. These templates capture the fundamental visual characteristics of different RSO types, separating the essential classification features from the need for extensive real-world imagery.
2Reliability
If traditional object detection methods are used, then labeled training data is required which is difficult to obtain, but this requirement increases the complexity and resource requirements of the system
Solution Approach 1:
The system generates its own training data through synthetic image generation using templates. Instead of relying on external labeled datasets, the system creates its own training materials with known ground truth, making the data preparation process self-sufficient and eliminating the need for manual annotation of real RSO images.
Solution Approach 2:
The patent performs preliminary actions by pre-defining templates that represent various RSO types before actual detection begins. These templates are created in advance and used to generate training data and establish classification criteria, preparing the system ahead of time without requiring complex post-processing or extensive labeled datasets.
3Measurement precision
If more real ground-truthed training data is collected to improve model training, then classification performance can be enhanced, but the time and resources required to curate and label this data increase significantly
Solution Approach 1:
Instead of collecting and labeling real RSO images, the system copies the essential visual characteristics of RSOs through synthesized images generated from templates. This approach creates unlimited training data instantaneously without the time-consuming processes of field collection, image acquisition, and manual labeling.
Solution Approach 2:
The patent changes the fundamental parameter of data representation from real photographed images to synthetically generated images based on templates. This parameter change transforms the data creation process from a time-intensive curation task to a computationally efficient generation process, maintaining classification performance while dramatically reducing data preparation time.
Data Source
AI summary
Embodiments can relate to a system for efficiently and dynamically classifying an object of an input image. The system can include an input module for receiving an image of an area of interest captured by an image capture device. The system can include a memory including a synthetic dataset composed of templates, wherein at least one template can be constructed of a statistical mean of shapes. The system can include a processor having a detection module and a classifier module. The detection module can be configured to scan an image to identify a streak pattern. The classifier module can be configured to compare a streak pattern to classify a streak pattern as being representative of a resident space object or representative of a star. The system can include a user interface configured to generate an output identifying a streak pattern as a resident space object or a star.