Closely-Spaced Object Classification Using MuyGPyS
Kerianne Pruett Nathan McNaughton Michael Schneider
Accurately detecting rendezvous and proximity operations (RPO) is crucial for
understanding how objects are behaving in the space domain. However, detecting
closely-spaced objects (CSO) is challenging for ground-based optical space
domain awareness (SDA) algorithms as two objects close together along the
line-of-sight can appear blended as a single object within the point-spread
function (PSF) of the optical system. Traditional machine learning methods can
be useful for differentiating between singular objects and closely-spaced
objects, but many methods require large training sample sizes or high
signal-to-noise conditions. The quality and quantity of realistic data make
probabilistic classification methods a superior approach, as they are better
suited to handle these data inadequacies. We present CSO classification results
using the Gaussian process python package, MuyGPyS, and examine classification
accuracy as a function of angular separation and magnitude difference between
the simulated satellites. This orbit-independent analysis is done on highly
accurate simulated SDA images that emulate realistic ground-based
commercial-of-the-shelf (COTS) optical sensor observations of CSOs. We find
that MuyGPyS outperforms traditional machine learning methods, especially under
more challenging circumstances.