Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Mathieu Turgeon-Pelchat"'
Autor:
Mikhail Sokolov, Christopher Henry, Joni Storie, Christopher Storie, Victor Alhassan, Mathieu Turgeon-Pelchat
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 482-492 (2023)
Deep learning has become one of remote sensing scientists' most efficient computer vision tools in recent years. However, the lack of training labels for the remote sensing datasets means that scientists need to solve the domain adaptation (DA) probl
Externí odkaz:
https://doaj.org/article/76d2c6ce3cdc466bb4b02b96172bd206
Publikováno v:
Canadian Journal of Remote Sensing, Vol 47, Iss 3, Pp 381-395 (2021)
Airborne LiDAR data allow the precise modeling of topography and are used in multiple contexts. To facilitate further analysis, the point cloud classification process allows the assignment of a class, object or feature, to each point. This research u
Externí odkaz:
https://doaj.org/article/48a30470fa86482ca63edb6b19c22a89
Autor:
Mathieu Turgeon-Pelchat, Heather McGrath, Fatemeh Esfahani, Simon Tolszczuk-Leclerc, Thomas Rainville, Nicolas Svacina, Lingjun Zhou, Zarrin Langari, Hospice Houngbo
The Canada Centre for Mapping and Earth Observation (CCMEO) uses Radarsat Constellation Mission (RCM) data for near-real time flood mapping. One of the many advantages of using SAR sensors, is that they are less affected by the cloud coverage and atm
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f4903c96405b717db4e6c83d6094119d
https://doi.org/10.5194/egusphere-egu23-9091
https://doi.org/10.5194/egusphere-egu23-9091
Publikováno v:
Canadian Journal of Remote Sensing. 47:381-395
Airborne LiDAR data allow the precise modeling of topography and are used in multiple contexts. To facilitate further analysis, the point cloud classification process allows the assignment of a cla...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c362311c314475b121ac140d8937109a
https://doi.org/10.1080/07038992.2021.1927687
https://doi.org/10.1080/07038992.2021.1927687
Autor:
Mikhail Sokolov, Christopher Henry, Joni Storie, Christopher Storie, Victor Alhassan, Mathieu Turgeon-Pelchat
Deep learning has become one of remote sensing scientists' most efficient computer vision tools in recent years. However, the lack of training labels for the remote sensing datasets means that scientists need to solve the domain adaptation problem to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bacab563580ca269ca0b9583ea9c6693
Autor:
Nouri Sabo, Mathieu Turgeon-Pelchat, Charles Papasodoro, Jean-Samuel Proulx-Bourque, Daniel Pilon, Lucie Mathieu
Publikováno v:
IGARSS
Natural Resources Canada is in the process of defining spatial resolution requirements for a national scale optical imagery service. This study aims to evaluate the impact of spatial resolution on residential buildings extraction accuracy in an urban
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0ef1b62a579867562583720f39d21b4e
https://doi.org/10.1109/igarss.2019.8899016
https://doi.org/10.1109/igarss.2019.8899016
Publikováno v:
IGARSS
This paper introduces the exploitation of a convolutional neural network for the extraction of topographic features from high-resolution optical satellite imagery. A UNET based model was trained for seven feature classes of roads, buildings, waterbod
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d2bf483e7bce3208ba78d90a875c96fa
https://doi.org/10.1109/igarss.2018.8519171
https://doi.org/10.1109/igarss.2018.8519171