Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Luigi Tommaso Luppino"'
Autor:
Santiago Cepeda, Luigi Tommaso Luppino, Ole Solheim, Angel Pérez-Núñez, Sergio García-García, Anna Karlberg, Live Eikenes, Tomas Zamora, Rosario Sarabia, Ignacio Arrese, Samuel Kuttner
Publikováno v:
Brain and Spine, Vol 3, Iss , Pp 101960- (2023)
Externí odkaz:
https://doaj.org/article/58e9482094514abc9d4e4f0344a64cdb
Autor:
Santiago Cepeda, Luigi Tommaso Luppino, Angel Pérez-Núñez, Ole Solheim, Sergio García-García, María Velasco-Casares, Anna Karlberg, Live Eikenes, Rosario Sarabia, Ignacio Arrese, Tomás Zamora, Pedro Gonzalez, Luis Jiménez-Roldán, Samuel Kuttner
Publikováno v:
Cancers
Cancers; Volume 15; Issue 6; Pages: 1894
Cancers; Volume 15; Issue 6; Pages: 1894
The globally accepted surgical strategy in glioblastomas is removing the enhancing tumor. However, the peritumoral region harbors infiltration areas responsible for future tumor recurrence. This study aimed to evaluate a predictive model that identif
Publikováno v:
Solar Energy. 198:81-92
Datasets from meteorological reanalyses and retrievals from satellites are the available sources of large-scale information about solar radiation. However, both the reanalyses and the satellite-based estimates can be severely biased, especially in hi
Autor:
Luigi Tommaso Luppino, Mads Adrian Hansen, Michael Kampffmeyer, Filippo Maria Bianchi, Gabriele Moser, Robert Jenssen, Stian Normann Anfinsen
Image translation with convolutional autoencoders has recently been used as an approach to multimodal change detection (CD) in bitemporal satellite images. A main challenge is the alignment of the code spaces by reducing the contribution of change pi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6e2b774997bbb2899b968acc51fc52a1
https://hdl.handle.net/11567/1093201
https://hdl.handle.net/11567/1093201
Several generic methods have recently been developed for change detection in heterogeneous remote sensing data, such as images from synthetic aperture radar (SAR) and multispectral radiometers. However, these are not well suited to detect weak signat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::29bda4b855b7443b61734b87c0ddb8f7
https://hdl.handle.net/10037/27111
https://hdl.handle.net/10037/27111
Publikováno v:
IGARSS
Change detection represents a major family of remote sensing image analysis techniques and plays a fundamental role in a variety of applications to environmental monitoring and disaster risk management. However, most change detection methods operate
Autor:
Luigi Tommaso Luppino, Mads A. Hansen, Stian Normann Anfinsen, Gabriele Moser, Sebastiano B. Serpico
Publikováno v:
2020 IEEE Radar Conference (RadarConf20).
A new methodology for unsupervised heterogeneous change detection has recently been proposed, which combines deep neural networks for domain alignment and image-to-image regression with a comparison of domain-specific pixel affinities to reveal struc
Autor:
Stian Normann Anfinsen, Luigi Tommaso Luppino, Robert Jenssen, Gabriele Moser, Sebastiano B. Serpico, Filippo Maria Bianchi, Michael Kampffmeyer
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a599615eabf7ed476dd0b113c0a93329
http://arxiv.org/abs/2001.04271
http://arxiv.org/abs/2001.04271
Autor:
Gabriele Moser, Stian Normann Anfinsen, Luigi Tommaso Luppino, F. Figari Tomenotti, Mads A. Hansen
Publikováno v:
IGARSS
This paper proposes a new method for bitemporal change detection in heterogeneous remote sensing images. A modified canonical correlation analysis is used to align the code layers of two deep convolutional autoencoders, one for each image domain. It
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e47f21ed0da0ac4f4b086dc43cacbd43
https://hdl.handle.net/11567/1043703
https://hdl.handle.net/11567/1043703
Publikováno v:
MLSP
Change detection in heterogeneous multitemporal satellite images is an emerging topic in remote sensing. In this paper we propose a framework, based on image regression, to perform change detection in heterogeneous multitemporal satellite images, whi