Classification of Low Earth Orbit (LEO) Resident Space Objects’ (RSO) Light Curves Using a Support Vector Machine (SVM) and Long Short-Term Memory (LSTM)
importance 4/10
ai assisted
Space Satellite Systems and Control space situational awareness stage: research
Problem
Classifying short light curves of low Earth orbit resident space objects to infer object type, attitude and shape.
Why it matters
LEO objects are numerous and short light curves are hard to analyze, so classification aids space monitoring and safety.
What the AI did
Wavelet scattering features extracted from LEO light curves fed into machine learning classifiers (SVM and LSTM) for classification.
Results
not stated in source
Impact
The evidence only supports a proposed novel classification approach, not demonstrated performance.
Record
| Venue | Sensors |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Randa Qashoa, Regina Lee |
| Citations | 27 (OpenAlex, at import time) |
| References | 46 |
| Model or method | SVM and LSTM with wavelet scattering transformation |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/s23146539 |
| Open access | gold |
| Keywords | light curves, LEO, SVM, LSTM, space situational awareness, classification |
Importance rating
4
real but narrower advance
LEO object classification supports space situational awareness and safety; abstract notes many RSOs are in LEO but gives no counts or impact figures.
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