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pro vyhledávání: '"background estimation"'
Longwave infrared (LWIR) hyperspectral imaging can be used for many tasks in remote sensing, including detecting and identifying effluent gases by LWIR sensors on airborne platforms. Once a potential plume has been detected, it needs to be identified
Externí odkaz:
http://arxiv.org/abs/2411.15378
Autor:
Das, Ranit, Finke, Thorben, Hein, Marie, Kasieczka, Gregor, Krämer, Michael, Mück, Alexander, Shih, David
Resonant anomaly detection methods have great potential for enhancing the sensitivity of traditional bump hunt searches. A key component of these methods is a high quality background template used to produce an anomaly score. Using the LHC Olympics R
Externí odkaz:
http://arxiv.org/abs/2411.00085
Publikováno v:
A&A 690, A250 (2024)
Estimation of the amount of cosmic-ray induced background events is a challenging task for Imaging Atmospheric Cherenkov Telescopes (IACTs). Most approaches rely on a model of the background signal derived from archival observations, which is then no
Externí odkaz:
http://arxiv.org/abs/2409.02527
Autor:
Sarkar, Arnab, Grant, Catherine E., Miller, Eric D., Bautz, Mark, Schneider, Benjamin, Foster, Rick F., Schellenberger, Gerrit, Allen, Steven, Kraft, Ralph P., Wilkins, Dan, Falcone, Abe, Ptak, Andrew
Galactic cosmic ray (GCR) particles have a significant impact on the particle-induced background of X-ray observatories, and their flux exhibits substantial temporal variability, potentially influencing background levels. In this study, we present on
Externí odkaz:
http://arxiv.org/abs/2405.06602
Deep learning identification models have shown promise for identifying gas plumes in Longwave IR hyperspectral images of urban scenes, particularly when a large library of gases are being considered. Because many gases have similar spectral signature
Externí odkaz:
http://arxiv.org/abs/2401.13068
A light CNN based on residual learning and background estimation for hyperspectral anomaly detection
Autor:
Jiajia Zhang, Pei Xiang, Jin Shi, Xiang Teng, Dong Zhao, Huixin Zhou, Huan Li, Jiangluqi Song
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 132, Iss , Pp 104069- (2024)
Existing deep learning-based hyperspectral anomaly detection methods typically perform anomaly detection by reconstructing a clean background. However, for the deep networks, there are many parameters that need to be adjusted. To reduce parameters of
Externí odkaz:
https://doaj.org/article/857513d7ec9845ecbc554eee222f7363
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Akademický článek
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Background estimation is essential when studying TeV gamma-ray astronomy for extensive air shower arrays. In this work, by applying four applying four different methods including equi-zenith angle method, surrounding window method, direct integration
Externí odkaz:
http://arxiv.org/abs/2210.00004
Publikováno v:
In Ore Geology Reviews June 2024 169