Aug 21, 2023 12:28 AM
(This post was last modified: Aug 21, 2023 12:30 AM by C C.)
https://www.universetoday.com/162816/wan...-learning/
EXCERPT: . . . This study is the latest in a series that addresses applications for hyperspectral imaging for activities in space. The first paper, “Intelligent characterization of space objects with hyperspectral imaging,” appeared in Acta Astronautica in February 2023 and was part of the Hyperspectral Imager for Space Surveillance and Tracking (HyperSST) project. This was one of thirteen debris mitigation concepts selected by the UK Space Agency (UKSA) for funding last year and is the precursor to the ESA’s Hyperspectral space debris Classification (HyperClass) project.
Their latest paper explored how this same imaging technique could be used in the growing field of UAP identification. This process consists of collecting and processing data from across the electromagnetic spectrum from single pixels, typically to identify different objects or materials captured in images. As Vasile explained to Universe Today via email, hyperspectral imaging paired with machine learning has the potential for narrowing the search for possible technosignatures by eliminating false positives caused by human-made debris objects (spent stages, defunct satellites, etc.):
“If UAP are space objects, then what we can do by analyzing the spectra is to understand the material composition even from a single pixel. We can also understand the attitude motion by analyzing the time variation of the spectra. Both things are very important because we can identify object by their spectral signature and understand their motion with minimal optical requirements.”
Vasile and his colleagues propose the creation of a data processing pipeline for processing UAP images using machine learning algorithms... (MORE - details)
EXCERPT: . . . This study is the latest in a series that addresses applications for hyperspectral imaging for activities in space. The first paper, “Intelligent characterization of space objects with hyperspectral imaging,” appeared in Acta Astronautica in February 2023 and was part of the Hyperspectral Imager for Space Surveillance and Tracking (HyperSST) project. This was one of thirteen debris mitigation concepts selected by the UK Space Agency (UKSA) for funding last year and is the precursor to the ESA’s Hyperspectral space debris Classification (HyperClass) project.
Their latest paper explored how this same imaging technique could be used in the growing field of UAP identification. This process consists of collecting and processing data from across the electromagnetic spectrum from single pixels, typically to identify different objects or materials captured in images. As Vasile explained to Universe Today via email, hyperspectral imaging paired with machine learning has the potential for narrowing the search for possible technosignatures by eliminating false positives caused by human-made debris objects (spent stages, defunct satellites, etc.):
“If UAP are space objects, then what we can do by analyzing the spectra is to understand the material composition even from a single pixel. We can also understand the attitude motion by analyzing the time variation of the spectra. Both things are very important because we can identify object by their spectral signature and understand their motion with minimal optical requirements.”
Vasile and his colleagues propose the creation of a data processing pipeline for processing UAP images using machine learning algorithms... (MORE - details)
