Characterization and Classification of Low-Resolution Satellites with Electro-Optical Fiducial Markers
Bibliographic record
Abstract
In this paper, we demonstrate the efficacy of identifying low-Earth-orbit and geosynchronous-Earth-orbit satellites with an electro-optical fiducial marker in a complex mission environment. Our focus is on space-based observations, where the observing satellite sensors produce low-resolution or unresolved images. To demonstrate this novel approach, we will use the digital mission engineering software tool Ansys Systems Tool Kit and its Electro-Optical Infrared capability to model missions, vary sensor properties, modify electro-optical fiducial markers on the satellite of interest, and generate synthetic sensor imagery data to train and evaluate support vector machine and convolutional neural network classifiers. We will also investigate how feature selection and machine-learning model performance is impacted using low-resolution/unresolved images and how well our models can distinguish the satellite with the electro-optical fiducial markers from the bloblike shapes. The approach discussed here will provide a generalized framework for configuring systems and for object identification and characterization. The primary application in this work is for space situational awareness and space domain awareness; however, the workflows can also be applied to object identification in other domains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".