An updated re-entry analysis of the Hubble Space Telescope

Baker, Kyle; Culton, Eryn; Lang, Jonathan; Lewis, Zachary A.; Perez-Alemany, Robert; Rizzo, Alexa; Smeresky, Brendon; Starks, Anthony; Teneyck, Joshua; Rhatigan, Jennifer L.; Romano, Marcello

doi:10.1016/j.jsse.2020.07.006

Cited by 2 publications

(2 citation statements)

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“…(Chen et al 2018 ). Several space agencies have conducted a series of successful space exploration missions like the super excited Apollo mission (Papanastassiou and Wasserburg 1971 ), Hubble mission (Baker et al 2020 ), Voyager mission (Cohen and Rymer 2020 ), Cassini-Huygens mission (Sotin et al 2021 ), Chandra mission (Tomsick et al 2021 ) of the National Aeronautics and Space Administration ( ). Aryabhata (Damle et al 1976 ), Chandrayaan 2 carried by GSLV Mark 3 (Chandrashekar 2016 ), Mangalyaan (Haider and Pandya 2015 ), launching 104 satellites in a single attempt (Muraleedharan et al 2019 ), etc., are prominent successful space missions conducted by the Indian Space Research Organization ( ).…”

Section: Introductionmentioning

confidence: 99%

Modeling of texture quantification and image classification for change prediction due to COVID lockdown using Skysat and Planetscope imagery

Shakya

Ramola

Vidyarthi

2021

Model. Earth Syst. Environ.

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This research work models two methods together to provide maximum information about a study area. The quantification of image texture is performed using the “grey level co-occurrence matrix ( )” technique. Image classification-based “object-based change detection ( )” methods are used to visually represent the developed transformation in the study area. Pre-COVID and post-COVID (during lockdown) panchromatic images of Connaught Place, New Delhi, are investigated in this research work to develop a model for the study area. Texture classification of the study area is performed based on visual texture features for eight distances and four orientations. Six different image classification methodologies are used for mapping the study area. These methodologies are “Parallelepiped classification ( ),” “Minimum distance classification ( ),” “Maximum likelihood classification ( ),” “Spectral angle mapper ( ),” “Spectral information divergence ( )” and “Support vector machine ( ).” calculations have provided a pattern in texture features contrast, correlation, , and . Maximum classification accuracy of and are obtained for pre-COVID and post-COVID image data through classification technique. Finally, a model is presented to analyze before and after COVID images to get complete information about the study area numerically and visually.

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Section: Introductionmentioning

confidence: 99%

Modeling of texture quantification and image classification for change prediction due to COVID lockdown using Skysat and Planetscope imagery

Shakya

Ramola

Vidyarthi

2021

Model. Earth Syst. Environ.

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“…Shara and Johnston 17 warned about this three decades ago: 'artificial Earth satellites will cross the fields of view of operating HST science instruments with distressingly high brightnesses and frequencies'. Launched in 1990, the orbit of HST is slowly decaying due to atmospheric drag 18 , now being at a mean altitude of 538 km above the surface of the Earth. HST is thus sensitive to other satellites situated in higher orbits that, depending on the solar illumination angle, position and telescope pointing, can affect the observations by causing bright streaks of light across the HST images.…”

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confidence: 99%

The impact of satellite trails on Hubble Space Telescope observations

et al. 2023

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The recent launch of low Earth orbit satellite constellations is creating a growing threat for astronomical observations with ground-based telescopes1–10 that has alarmed the astronomical community 11–16. Observations affected by artificial satellites can become unusable for scientific research, wasting a growing fraction of the research budget on costly infrastructures and mitigation efforts. Here we report the first measurements, to our knowledge, of artificial satellite contamination on observations from a low Earth orbit made with the Hubble Space Telescope. With the help of volunteers on a citizen science project (www.asteroidhunter.org) and a deep learning algorithm, we scanned the archive of Hubble Space Telescope images taken between 2002 and 2021. We find that a fraction of 2.7% of the individual exposures with a typical exposure time of 11 minutes are crossed by satellites and that the fraction of satellite trails in the images increases with time. This fraction depends on the size of the field of view, exposure time, filter used and pointing. With the growing number of artificial satellites currently planned, the fraction of Hubble Space Telescope images crossed by satellites will increase in the next decade and will need further close study and monitoring.

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An updated re-entry analysis of the Hubble Space Telescope

Cited by 2 publications

References 1 publication

Modeling of texture quantification and image classification for change prediction due to COVID lockdown using Skysat and Planetscope imagery

Modeling of texture quantification and image classification for change prediction due to COVID lockdown using Skysat and Planetscope imagery

The impact of satellite trails on Hubble Space Telescope observations

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