Zobrazeno 1 - 10
of 162
pro vyhledávání: '"Frédéric PASCAL"'
Publikováno v:
Revue Française de Photogrammétrie et de Télédétection, Iss 209 (2015)
A statistical model for detecting changes in remote sensing images has recently been proposed in (Prendes et al., 2014a,b). This model is sufficiently general to be used for homogeneous images acquired by the same kind of sensors (e.g., two optical i
Externí odkaz:
https://doaj.org/article/55a33117d5244e1992ecd1a78bbbe688
Expectation-Maximization (EM) algorithm is a widely used iterative algorithm for computing (local) maximum likelihood estimate (MLE). It can be used in an extensive range of problems, including the clustering of data based on the Gaussian mixture mod
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c52857e81a37c88e07d60251fd207eee
http://arxiv.org/abs/2303.14989
http://arxiv.org/abs/2303.14989
Publikováno v:
Détection de changements et analyse des séries temporelles d’images 1 ISBN: 9781789480566
Ce chapitre propose un aperçu des méthodologies de comparaisons basées sur les matrices de covariance locale des pixels multivariés. L'étendue de cet aperçu est spécifiée par les conditions suivantes : 1) l'accent est mis sur les méthodologi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::992c1d36e9a402f6867fbee1c099058c
https://doi.org/10.51926/iste.9056.ch3
https://doi.org/10.51926/iste.9056.ch3
Publikováno v:
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2020, 68, pp.5003-5015. ⟨10.1109/TSP.2020.3019110⟩
IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2020, 68, pp.5003-5015. ⟨10.1109/TSP.2020.3019110⟩
International audience; Covariance matrices play a major role in statistics , signal processing and machine learning applications. This paper focuses on the semiparametric covariance/scatter matrix estimation problem in elliptical distributions. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::48cf9e7d07aff6079c8dbad8be481391
https://doi.org/10.1109/tsp.2020.3019110
https://doi.org/10.1109/tsp.2020.3019110
This paper tackles the problem of missing data imputation for noisy and non-Gaussian data. A classical imputation method, the Expectation Maximization (EM) algorithm for Gaussian mixture models, has shown interesting properties when compared to other
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0863a03fb2841866a3258424ee589192
An Overview of Covariance‐based Change Detection Methodologies in Multivariate SAR Image Time Series
Publikováno v:
Change Detection and Image Time Series Analysis 1
Change Detection and Image Time Series Analysis 1, Wiley, 2021, ⟨10.1002/9781119882268.ch3⟩
Change Detection and Image Time Series Analysis 1, 1, Wiley, 2021, ⟨10.1002/9781119882268.ch3⟩
Change Detection and Image Time Series Analysis 1, Wiley, 2021, ⟨10.1002/9781119882268.ch3⟩
Change Detection and Image Time Series Analysis 1, 1, Wiley, 2021, ⟨10.1002/9781119882268.ch3⟩
International audience; Change detection (CD) for remotely sensed images of the Earth has been a popular subject of study in the past decades. With the increase in the number of spatial missions with embedded synthetic aperture radar (SAR) sensors, t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3fa1dbfd13247bc2dd99750b4f49632f
https://hal-centralesupelec.archives-ouvertes.fr/hal-03519630
https://hal-centralesupelec.archives-ouvertes.fr/hal-03519630
Publikováno v:
Signal Processing
Signal Processing, 2021, 187, pp.108-116. ⟨10.1016/j.sigpro.2021.108116⟩
Signal Processing, Elsevier, 2021, 187, pp.108-116. ⟨10.1016/j.sigpro.2021.108116⟩
Signal Processing, 2021, 187, pp.108-116. ⟨10.1016/j.sigpro.2021.108116⟩
Signal Processing, Elsevier, 2021, 187, pp.108-116. ⟨10.1016/j.sigpro.2021.108116⟩
International audience; In this paper, we propose a new estimator of the covariance matrix parameters when observations follow a mixture of a deterministic Compound-Gaussian (CG) and a white Gaussian noise. In particular, the covariance matrix of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa3e5768855cedfd4d3515491b9378ca
https://hal.univ-grenoble-alpes.fr/hal-03273066
https://hal.univ-grenoble-alpes.fr/hal-03273066
Publikováno v:
Journal of Signal Processing Systems
Journal of Signal Processing Systems, Springer, 2021, ⟨10.1007/s11265-021-01674-y⟩
Journal of Signal Processing Systems, Springer, 2021, ⟨10.1007/s11265-021-01674-y⟩
The joint estimation of the location vector and the shape matrix of a set of independent and identically Complex Elliptically Symmetric (CES) distributed observations is investigated from both the theoretical and computational viewpoints. This joint
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::603748f41f0c41eb8b23840ee6cd7c51
https://hal.archives-ouvertes.fr/hal-03223839/document
https://hal.archives-ouvertes.fr/hal-03223839/document
Publikováno v:
SSP
2021 IEEE Statistical Signal Processing Workshop (SSP)
2021 IEEE Statistical Signal Processing Workshop (SSP), Jul 2021, Rio de Janeiro, Brazil. pp.56-60, ⟨10.1109/SSP49050.2021.9513856⟩
2021 IEEE Statistical Signal Processing Workshop (SSP)
2021 IEEE Statistical Signal Processing Workshop (SSP), Jul 2021, Rio de Janeiro, Brazil. pp.56-60, ⟨10.1109/SSP49050.2021.9513856⟩
International audience; Change detection for radar image time series is an important task that can help to monitor deforestation and global warming consequences. We present a method to detect changes in time for Polarimetric SAR images based on a clu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::050cd1ec52f76bda04b30f6db777d593
https://doi.org/10.1109/ssp49050.2021.9513856
https://doi.org/10.1109/ssp49050.2021.9513856
Publikováno v:
IEEE Radar Conference 2021
IEEE Radar Conference 2021, May 2021, ATLANTA (virtual), United States
IEEE Radar Conference 2021, May 2021, ATLANTA (virtual), United States
International audience; In this paper, a two-step methodology is developed to deinterlace RADAR signals. We mainly worked from a data simulator to obtain a large diversity of signals representing typical RADARs. First, a clustering algorithm is used
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8890da9b0677568170128913d5a1faa0
https://doi.org/10.1109/radarconf2147009.2021.9455272
https://doi.org/10.1109/radarconf2147009.2021.9455272