Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Brian D Bue"'
Autor:
Andrew K Thorpe, Riley M Duren, Stephen Conley, Kuldeep R Prasad, Brian D Bue, Vineet Yadav, Kelsey T Foster, Talha Rafiq, Francesca M Hopkins, Mackenzie L Smith, Marc L Fischer, David R Thompson, Christian Frankenberg, Ian B McCubbin, Michael L Eastwood, Robert O Green, Charles E Miller
Publikováno v:
Environmental Research Letters, Vol 15, Iss 4, p 045005 (2020)
Accurate and timely detection, quantification, and attribution of methane emissions from Underground Gas Storage (UGS) facilities is essential for improving confidence in greenhouse gas inventories, enabling emission mitigation by facility operators,
Externí odkaz:
https://doaj.org/article/e7d67185b0d844e18b420f8253db0390
Publikováno v:
Remote Sensing, Vol 13, Iss 12, p 2364 (2021)
In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison wi
Externí odkaz:
https://doaj.org/article/ceb91922318c45599a86f6edc01e5713
Autor:
John W. Chapman, David R. Thompson, Mark C. Helmlinger, Brian D. Bue, Robert O. Green, Michael L. Eastwood, Sven Geier, Winston Olson-Duvall, Sarah R. Lundeen
Publikováno v:
Remote Sensing, Vol 11, Iss 18, p 2129 (2019)
We describe advanced spectral and radiometric calibration techniques developed for NASA’s Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). By employing both statistically rigorous analysis and utilizing in situ data to in
Externí odkaz:
https://doaj.org/article/393cbbfd93af4931b7564e9c81db7774
Autor:
James F. Bell, S. Jacob, Heni Ben Amor, Kiri L. Wagstaff, Chiman Kwan, Danika Wellington, Paul Horton, Brian D. Bue, Hannah Kerner
Publikováno v:
Data Mining and Knowledge Discovery. 34:1642-1675
Science teams for rover-based planetary exploration missions like the Mars Science Laboratory Curiosity rover have limited time for analyzing new data before making decisions about follow-up observations. There is a need for systems that can rapidly
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd5a7d1c27768edfa91497b7ac39fb9a
https://doi.org/10.1007/s10618-020-00697-6
https://doi.org/10.1007/s10618-020-00697-6
Publikováno v:
Remote Sensing, Vol 13, Iss 2364, p 2364 (2021)
Remote Sensing; Volume 13; Issue 12; Pages: 2364
Remote Sensing; Volume 13; Issue 12; Pages: 2364
In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison wi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01e6c21bdca91ce8d52d2a47e8629f62
https://www.mdpi.com/2072-4292/13/12/2364
https://www.mdpi.com/2072-4292/13/12/2364
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 12:3900-3918
Ongoing planetary exploration missions are returning large volumes of image data. Identifying surface changes in these images, e.g., new impact craters, is critical for investigating many scientific hypotheses. Traditional approaches to change detect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::143b904d8c896e62edca3c8d90dda163
https://doi.org/10.1109/jstars.2019.2936771
https://doi.org/10.1109/jstars.2019.2936771
Autor:
Brian D. Bue, Michael Denbina, Matthew Thill, Neda Kasraee, Annemarie Peacock, Zaid J. Towfic, Yunling Lou
Publikováno v:
IGARSS
We have mapped flooded areas in data collected by the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) using two convolutional neural network (CNN) image classifier architectures: U-Net and SegNet. Our study area was a region aro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b5122603b0d20a0ec39a0585d16c4d0d
https://doi.org/10.1109/igarss39084.2020.9324379
https://doi.org/10.1109/igarss39084.2020.9324379
Autor:
Andrew K. Thorpe, Christian Frankenberg, Robert O. Green, C. Elder, Riley M. Duren, David R. Thompson, Charles E. Miller, Philip E. Dennison, Glynn Hulley, Simon J. Hook, Brian D. Bue
Publikováno v:
IGARSS
The last five years have seen dramatic growth in the use of Visible Shortwave Infrared (VSWIR) and Thermal Infrared (TIR) imaging spectrometers to detect and characterize greenhouse methane sources. Targets include: dairy and animal husbandry emissio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fa00f1adec4e83cc3e91d5d91700f96f
https://doi.org/10.1109/igarss39084.2020.9323285
https://doi.org/10.1109/igarss39084.2020.9323285
Autor:
Talha Rafiq, David R. Thompson, Ian B. McCubbin, Marc Fischer, Kuldeep R. Prasad, K. T. Foster, Brian D. Bue, Stephen Conley, Francesca M. Hopkins, Andrew K. Thorpe, Mackenzie L. Smith, Robert O. Green, Charles E. Miller, Vineet Yadav, Riley M. Duren, Michael L. Eastwood, Christian Frankenberg
Publikováno v:
Environmental Research Letters, vol 15, iss 4
Accurate and timely detection, quantification, and attribution of methane emissions from Underground Gas Storage (UGS) facilities is essential for improving confidence in greenhouse gas inventories, enabling emission mitigation by facility operators,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bc9f43a553ef3984c60fb1507aaa0478
https://escholarship.org/uc/item/7mm2f36f
https://escholarship.org/uc/item/7mm2f36f
Autor:
Talha Rafiq, Kevin Gill, Charles E. Miller, Riley M. Duren, Aaron Plave, Joshua Rodriguez, Francesca M. Hopkins, Brian D. Bue, Daniel H. Cusworth, Robert Tapella, K. T. Foster, Andrew K. Thorpe, Vineet Yadav
The Methane Source Finder is a web-based data portal developed under NASA’s CMS and ACCESS programs for exploring methane data in the state of California. This open access interactive map allows users to discover, analyze, and download data across
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aeb0c019a78f3f0fbfae7a2c6fedaae4
https://doi.org/10.5194/egusphere-egu2020-9923
https://doi.org/10.5194/egusphere-egu2020-9923