Zobrazeno 1 - 10
of 59
pro vyhledávání: '"S. Boukir"'
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXVIII-3/W22, Pp 49-54 (2013)
An object-based approach for forest disaster change detection using High Resolution (HR) satellite images is proposed. An automatic feature selection process is used to optimize image segmentation via an original calibration-like procedure. A multite
Externí odkaz:
https://doaj.org/article/77f1b5b6190e44608e8f06dca184dda2
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol I-7, Pp 1-6 (2012)
The main goal of this study is to define a method to describe the forest structure of maritime pine stands from Very High Resolution satellite imagery. The emphasis is placed on the automatisation of the process to identify the most relevant image fe
Externí odkaz:
https://doaj.org/article/98be71359bfd43d18ddc2e1a1ac73902
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol I-7, Pp 111-116 (2012)
This paper presents a new unsupervised classification method which aims to effectively and efficiently map remote sensing data. The Mean-Shift (MS) algorithm, a non parametric density-based clustering technique, is at the core of our method. This pow
Externí odkaz:
https://doaj.org/article/fb1ba1d1e2dc4d55a38c853216b1da24
Publikováno v:
Photogrammetric Engineering & Remote Sensing. 87:841-852
In this article, margin theory is exploited to design better ensemble classifiers for remote sensing data. A semi-supervised version of the ensemble margin is at the core of this work. Some major challenges in ensemble learning are investigated using
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2c1d1045abe3332f18c096fc7ce33ad4
https://doi.org/10.14358/pers.20-00071r3
https://doi.org/10.14358/pers.20-00071r3
Identifying and Correcting Mislabeled Satellite Image Data by Iterative Ordering of Ensemble Margins
Publikováno v:
IGARSS
The accuracy of a supervised classifier is directly influenced by the quality of the training data used. However, real-world data often suffers from mislabelling issues. To handle the mislabeling problem, we propose an ensemble margin-based mislabele
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e46d5440c6dcc733b176059e8e0892a8
https://doi.org/10.1109/igarss.2019.8898167
https://doi.org/10.1109/igarss.2019.8898167
Publikováno v:
IGARSS
The problem of class imbalance is often encountered in remote sensing data and has a negative effect on the classification performance of supervised classifiers even in ensemble models. The ensemble margin is a fundamental concept in ensemble learnin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::40a3fd857339454c70e7ce33a5c93a17
https://doi.org/10.1109/igarss.2019.8898652
https://doi.org/10.1109/igarss.2019.8898652
Akademický článek
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Akademický článek
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Publikováno v:
IFAC Proceedings Volumes. 26:139-144
This paper presents an active approach for the task of computing the 3-D structure of a nuclear plant environment from an image sequence, more precisely the 3-D structure of cylindrical objects. Active vision is considered by Computing adequate camer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::980964613121805a56a476a0b66d4f08
https://doi.org/10.1016/s1474-6670(17)49289-5
https://doi.org/10.1016/s1474-6670(17)49289-5
Autor:
F. Cheneviere, S. Boukir
Publikováno v:
Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2bc21b85b678fb57beeadf45ca78709e
https://doi.org/10.1109/icpr.2004.1333829
https://doi.org/10.1109/icpr.2004.1333829