Zobrazeno 1 - 10
of 105
pro vyhledávání: '"Mathieu Fauvel"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 2980-2994 (2024)
In this article, we propose a method exploiting irregular and unaligned Sentinel-2 satellite image time series (SITS) for large-scale land cover pixel-based classification. We perform end-to-end learning by combining a time and space informed kernel
Externí odkaz:
https://doaj.org/article/c1c176894458402eb8b83a3da2500f5f
Publikováno v:
Remote Sensing, Vol 12, Iss 14, p 2326 (2020)
Accounting for endmember variability is a challenging issue when unmixing hyperspectral data. This paper models the variability that is associated with each endmember as a conical hull defined by extremal pixels from the data set. These extremal pixe
Externí odkaz:
https://doaj.org/article/400fba97182b411d9c4e698645baad5c
Autor:
Clément Mallet, Nesrine Chehata, Mauro Dalla Mura, Jean-Emmanuel Deschaud, Ronan Fablet, Mathieu Fauvel, Cyrielle Guérin, Emmanuel Trouvé
Publikováno v:
Revue Française de Photogrammétrie et de Télédétection, Iss 217-218 (2018)
Externí odkaz:
https://doaj.org/article/cc5d50687e424cccba741f94e67bf14b
Autor:
Nicolas Karasiak, Jean-François Dejoux, Mathieu Fauvel, Jérôme Willm, Claude Monteil, David Sheeren
Publikováno v:
Remote Sensing, Vol 11, Iss 21, p 2512 (2019)
Mapping forest composition using multiseasonal optical time series remains a challenge. Highly contrasted results are reported from one study to another suggesting that drivers of classification errors are still under-explored. We evaluated the perfo
Externí odkaz:
https://doaj.org/article/2532107be48b466d9e8ca3efac669e29
Publikováno v:
Remote Sensing, Vol 9, Iss 10, p 993 (2017)
Grasslands represent a significant source of biodiversity that is important to monitor over large extents. The Spectral Variation Hypothesis (SVH) assumes that the Spectral Heterogeneity (SH) measured from remote sensing data can be used as a proxy f
Externí odkaz:
https://doaj.org/article/c1e682c2441c4e47bd45c163b3a9bdff
Publikováno v:
Remote Sensing, Vol 9, Iss 7, p 688 (2017)
This paper deals with the classification of grasslands using high resolution satellite image time series. Grasslands considered in this work are semi-natural elements in fragmented landscapes, i.e., they are heterogeneous and small elements. The firs
Externí odkaz:
https://doaj.org/article/23a0b569a46d4e6ca6c81480028d7ca6
Publikováno v:
Revue Française de Photogrammétrie et de Télédétection, Iss 208 (2014)
La détection des haies à partir d'images Pléiades est abordée dans cet article. Un système de détection en deux étapes est proposé. Il est basé sur l'utilisation conjointe de l'information spatiale et de l'information spectrale. Tout d'abord
Externí odkaz:
https://doaj.org/article/b1adaa594fc44ae2a0a093f4e35cde2e
Autor:
David Sheeren, Mathieu Fauvel, Veliborka Josipović, Maïlys Lopes, Carole Planque, Jérôme Willm, Jean-François Dejoux
Publikováno v:
Remote Sensing, Vol 8, Iss 9, p 734 (2016)
Mapping forest composition is a major concern for forest management, biodiversity assessment and for understanding the potential impacts of climate change on tree species distribution. In this study, the suitability of a dense high spatial resolution
Externí odkaz:
https://doaj.org/article/d1dc77aae4fe444c82009cf0e0e245e4
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2009 (2009)
Kernel principal component analysis (KPCA) is investigated for feature extraction from hyperspectral remote sensing data. Features extracted using KPCA are classified using linear support vector machines. In one experiment, it is shown that kernel pr
Externí odkaz:
https://doaj.org/article/e53baaa8f833424ebac4986c0671496f
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing, 2023, ⟨10.1109/TGRS.2023.3234527⟩
IEEE Transactions on Geoscience and Remote Sensing, 2023, ⟨10.1109/TGRS.2023.3234527⟩
In this article, we propose an approach based on Gaussian Processes (GP) for large scale land cover pixel-basedclassification with Sentinel-2 satellite image time-series (SITS). We used a sparse approximation of the posterior combined with variationa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c5e96ae81f374287cdf5253a30569bae
https://hal.science/hal-03781332v2/file/article_TGRS_v2.pdf
https://hal.science/hal-03781332v2/file/article_TGRS_v2.pdf
