Learned satellite identification using low-resolution spectropolarimetry

Ryan Swindle; J. Zachary Gazak; Matthew Phelps; Ian McQuaid; Justin Fletcher

doi:10.1117/12.2618956

3 June 2022 Learned satellite identification using low-resolution spectropolarimetry

Ryan Swindle, J. Zachary Gazak, Matthew Phelps, Ian McQuaid, Justin Fletcher

Proceedings Volume 12112, Polarization: Measurement, Analysis, and Remote Sensing XV; 121120H (2022) https://doi.org/10.1117/12.2618956
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

We introduce two new tools to the application of polarimetry to space domain awareness (SDA), the LoVIS spectropolarimeter on the 3.6 m AEOS telescope and deep convolutional neural networks (CNNs). Using a dataset of 20,000 simulated satellite observations, we train a CNN to map distance-invariant spectropolarimetric data to object identity. We report the classification accuracy of this simulation for a 9-class satellite problem, comparing results against low-resolution spectra for which prior success has been demonstrated as well as solar phase angle and satellite apparent magnitude. These initial experiments show potential for improved discrimination against nearly identical satellites on the basis of added polarimetric data.

Conference Presentation

Citation Download Citation

Ryan Swindle, J. Zachary Gazak, Matthew Phelps, Ian McQuaid, and Justin Fletcher "Learned satellite identification using low-resolution spectropolarimetry", Proc. SPIE 12112, Polarization: Measurement, Analysis, and Remote Sensing XV, 121120H (3 June 2022); https://doi.org/10.1117/12.2618956

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