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pro vyhledávání: '"Justo, Jon A."'
Autor:
Grigore, Diana-Nicoleta, Georgescu, Mariana-Iuliana, Justo, Jon Alvarez, Johansen, Tor, Ionescu, Andreea Iuliana, Ionescu, Radu Tudor
Few-shot knowledge distillation recently emerged as a viable approach to harness the knowledge of large-scale pre-trained models, using limited data and computational resources. In this paper, we propose a novel few-shot feature distillation approach
Externí odkaz:
http://arxiv.org/abs/2404.09326
Recent advancements in deep learning techniques have spurred considerable interest in their application to hyperspectral imagery processing. This paper provides a comprehensive review of the latest developments in this field, focusing on methodologie
Externí odkaz:
http://arxiv.org/abs/2404.06526
Autor:
Kovac, Daniel, Mucha, Jan, Justo, Jon Alvarez, Mekyska, Jiri, Galaz, Zoltan, Novotny, Krystof, Pitonak, Radoslav, Knezik, Jan, Herec, Jonas, Johansen, Tor Arne
This article explores the latest Convolutional Neural Networks (CNNs) for cloud detection aboard hyperspectral satellites. The performance of the latest 1D CNN (1D-Justo-LiuNet) and two recent 2D CNNs (nnU-net and 2D-Justo-UNet-Simple) for cloud segm
Externí odkaz:
http://arxiv.org/abs/2403.08695
Autor:
Justo, Jon Alvarez, Orlandic, Milica
Hyperspectral Imaging (HSI) is used in a wide range of applications such as remote sensing, yet the transmission of the HS images by communication data links becomes challenging due to the large number of spectral bands that the HS images contain tog
Externí odkaz:
http://arxiv.org/abs/2401.14786
Hyperspectral Imaging comprises excessive data consequently leading to significant challenges for data processing, storage and transmission. Compressive Sensing has been used in the field of Hyperspectral Imaging as a technique to compress the large
Externí odkaz:
http://arxiv.org/abs/2401.14762
Autor:
Justo, Jon Alvarez, Ghita, Alexandru, Kovac, Daniel, Garrett, Joseph L., Georgescu, Mariana-Iuliana, Gonzalez-Llorente, Jesus, Ionescu, Radu Tudor, Johansen, Tor Arne
Satellites are increasingly adopting on-board AI to optimize operations and increase autonomy through in-orbit inference. The use of Deep Learning (DL) models for segmentation in hyperspectral imagery offers advantages for remote sensing applications
Externí odkaz:
http://arxiv.org/abs/2310.16210
Autor:
Justo, Jon A., Garrett, Joseph, Langer, Dennis D., Henriksen, Marie B., Ionescu, Radu T., Johansen, Tor A.
Hyperspectral Imaging, employed in satellites for space remote sensing, like HYPSO-1, faces constraints due to few labeled data sets, affecting the training of AI models demanding these ground-truth annotations. In this work, we introduce The HYPSO-1
Externí odkaz:
http://arxiv.org/abs/2308.13679
Publikováno v:
In Geotextiles and Geomembranes August 2024 52(4):451-464
Akademický článek
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