SpectraNet: Learned Recognition of Artificial Satellites from High Contrast Spectroscopic Imagery

Author:

Gazak J. Zachary1,McQuaid Ian2,Swindle Ryan1,Phelps Matthew1,Fletcher Justin1

Affiliation:

1. United States Space Force Space Systems Command

2. Air Force Research Laboratory

Publisher

IEEE

Reference26 articles.

1. Exploiting Spatial Information in Raw Spectroscopic Imagery using Convolutional Neural;zachary gazak;AMOS,2020

2. Deep Residual Learning for Image Recognition;he,2015

3. Averaging Weights Leads to Wider Optima and Better Generalization;izmailov,2018

4. An advanced scattered moonlight model for Cerro Paranal;jones;Astronomy and Astrophysics,2013

5. The Discovery of Quasars and its Aftermath;kellermann;Journal of Astronomical History and Heritage,2014

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2. Seeing Stars: Learned Star Localization for Narrow-Field Astrometry;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

3. SPECTRANET-SO(3): Learning Satellite Orientation from Optical Spectra by Implicitly Modeling Mutually Exclusive Probability Distributions on The Rotation Manifold;ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2023-06-04

4. Adaptive fusion of multi-exposure images based on perceptron model;Applied Mathematics and Nonlinear Sciences;2023-04-28

5. Electro-Optical Fiducial Markers on Satellites for Identification and Characterization;AIAA SCITECH 2023 Forum;2023-01-19

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