Zobrazeno 1 - 10
of 453
pro vyhledávání: '"Gloaguen, Richard"'
Monitoring changes triggered by mining activities is crucial for industrial controlling, environmental management and regulatory compliance, yet it poses significant challenges due to the vast and often remote locations of mining sites. Remote sensin
Externí odkaz:
http://arxiv.org/abs/2407.03971
Autor:
Arbash, Elias, Fuchs, Margret, Rasti, Behnood, Lorenz, Sandra, Ghamisi, Pedram, Gloaguen, Richard
Addressing the critical theme of recycling electronic waste (E-waste), this contribution is dedicated to developing advanced automated data processing pipelines as a basis for decision-making and process control. Aligning with the broader goals of th
Externí odkaz:
http://arxiv.org/abs/2401.06528
In contrast to the well-investigated field of SAR-to-Optical translation, this study explores the lesser-investigated domain of Optical-to-SAR translation, a challenging field due to the ill-posed nature of this translation. The complexity arises as
Externí odkaz:
http://arxiv.org/abs/2401.00440
Autor:
Arbash, Elias, Ribeiro, Andréa de Lima, Thiele, Sam, Gnann, Nina, Rasti, Behnood, Fuchs, Margret, Ghamisi, Pedram, Gloaguen, Richard
The presence of undesired background areas associated with potential noise and unknown spectral characteristics degrades the performance of hyperspectral data processing. Masking out unwanted regions is key to addressing this issue. Processing only r
Externí odkaz:
http://arxiv.org/abs/2311.03053
Autor:
Koirala, Bikram, Rasti, Behnood, Bnoulkacem, Zakaria, Ribeiro, Andrea de Lima, Madriz, Yuleika, Herrmann, Erik, Gestels, Arthur, De Kerf, Thomas, Lorenz, Sandra, Fuchs, Margret, Janssens, Koen, Steenackers, Gunther, Gloaguen, Richard, Scheunders, Paul
Optical hyperspectral cameras capture the spectral reflectance of materials. Since many materials behave as heterogeneous intimate mixtures with which each photon interacts differently, the relationship between spectral reflectance and material compo
Externí odkaz:
http://arxiv.org/abs/2309.03216
Autor:
Afifi, Ahmed J., Thiele, Samuel T., Rizaldy, Aldino, Lorenz, Sandra, Ghamisi, Pedram, Tolosana-Delgado, Raimon, Kirsch, Moritz, Gloaguen, Richard, Heizmann, Michael
The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping appro
Externí odkaz:
http://arxiv.org/abs/2305.09928
Autor:
Coquelin, Daniel, Rasti, Behnood, Götz, Markus, Ghamisi, Pedram, Gloaguen, Richard, Streit, Achim
As with any physical instrument, hyperspectral cameras induce different kinds of noise in the acquired data. Therefore, Hyperspectral denoising is a crucial step for analyzing hyperspectral images (HSIs). Conventional computational methods rarely use
Externí odkaz:
http://arxiv.org/abs/2204.06979
Autor:
de Lima Ribeiro, Andréa, Fuchs, Margret C., Lorenz, Sandra, Röder, Christian, Heitmann, Johannes, Gloaguen, Richard
Publikováno v:
In Waste Management 15 April 2024 178:239-256
Autor:
Gairola, Gaurav Siddharth, Thiele, Samuel T., Khanna, Pankaj, Ramdani, Ahmad, Gloaguen, Richard, Vahrenkamp, Volker
Publikováno v:
In Marine and Petroleum Geology March 2024 161
The inclusion of spatial information into spectral classifiers for fine-resolution hyperspectral imagery has led to significant improvements in terms of classification performance. The task of spectral-spatial hyperspectral image classification has r
Externí odkaz:
http://arxiv.org/abs/2010.12337