Machine Vision and Deep Learning for Classification of Radio SETI Signals

• Main Authors: Harp, G. R, Richards, Jon, Tarter, Seth Shostak Jill C, Mackintosh, Graham, Scargle, Jeffrey D, Henze, Chris, Nelson, Bron, Cox, G. A, Egly, S, Vinodababu, S, Voien, J
• Format: Journal Article
• Language: English
• Published: 06-02-2019
• Subjects: Physics - Earth and Planetary Astrophysics; Physics - Instrumentation and Methods for Astrophysics
• DOI: 10.48550/arxiv.1902.02426

We apply classical machine vision and machine deep learning methods to
prototype signal classifiers for the search for extraterrestrial intelligence.
Our novel approach uses two-dimensional spectrograms of measured and simulated
radio signals bearing the imprint of a technological origin. The studies are
performed using archived narrow-band signal data captured from real-time SETI
observations with the Allen Telescope Array and a set of digitally simulated
signals designed to mimic real observed signals. By treating the 2D spectrogram
as an image, we show that high quality parametric and non-parametric
classifiers based on automated visual analysis can achieve high levels of
discrimination and accuracy, as well as low false-positive rates. The (real)
archived data were subjected to numerous feature-extraction algorithms based on
the vertical and horizontal image moments and Huff transforms to simulate
feature rotation. The most successful algorithm used a two-step process where
the image was first filtered with a rotation, scale and shift-invariant affine
transform followed by a simple correlation with a previously defined set of
labeled prototype examples. The real data often contained multiple signals and
signal ghosts, so we performed our non-parametric evaluation using a simpler
and more controlled dataset produced by simulation of complex-valued voltage
data with properties similar to the observed prototypes. The most successful
non-parametric classifier employed a wide residual (convolutional) neural
network based on pre-existing classifiers in current use for object detection
in ordinary photographs. These results are relevant to a wide variety of
research domains that already employ spectrogram analysis from time-domain
astronomy to observations of earthquakes to animal vocalization analysis.