Multisource Images Analysis Using Collaborative Clustering

Autor: Pierre Gançarski, Cédric Wemmert, Germain Forestier

Publikováno v: EURASIP Journal on Advances in Signal Processing, Vol 2008 (2008)

The development of very high-resolution (VHR) satellite imagery has produced a huge amount of data. The multiplication of satellites which embed different types of sensors provides a lot of heterogeneous images. Consequently, the image analyst has of

Externí odkaz: https://doaj.org/article/cc68f70cce2246fcba8486f839b5a422

CDPS: Constrained DTW-Preserving Shapelets

Autor: Hussein El Amouri, Thomas Lampert, Pierre Gançarski, Clément Mallet

Publikováno v: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2022
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2022, Jun 2022, Strasbourg, France
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Machine Learning and Knowledge Discovery in Databases ISBN: 9783031263866

The analysis of time series for clustering and classificationis becoming ever more popular because of the increasingly ubiquitousnature of IoT, satellite constellations, and handheld and smart-wearabledevices, etc. The presence of phase shift, differ

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e9120fb766ddc427201b095f9d18b1bb
https://hal.science/hal-03736948/document

Constrained Distance-Based Clustering for Satellite Image Time-Series

Autor: Thomas Lampert, Pierre Gançarski, Baptiste Lafabregue, Nicolas Serrette, Thi-Bich-Hanh Dao, Christel Vrain

Publikováno v: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12 (11), pp.4606-4621. ⟨10.1109/JSTARS.2019.2950406⟩
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2019, 12 (11), pp.4606-4621. ⟨10.1109/JSTARS.2019.2950406⟩

International audience; The advent of high-resolution instruments for time-series sampling poses added complexity for the formal definition of thematic classes in the remote sensing domain-required by supervised methods-while unsupervised methods ign

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e6ff36b54a7a1fca70aba9c247b7efa
https://doi.org/10.1109/jstars.2019.2950406

Classification de séries temporelles hétérogènes pour le suivi de l'état des cours d'eau

Autor: Agnès Braud, Pierre Gançarski, Corinne Grac, Agnès Herrmann, Florence Le Ber, Harrison Vernier

Publikováno v: Extraction et Gestion de Connaissance, EGC'2021
Extraction et Gestion de Connaissance, EGC'2021, Jan 2021, Montpellier, France
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This article is about a collaborative process and tools we have built to adapt a clustering method for analysing temporal sequences of physico-chemical measurements done on river streams. These data are characterised by sampling variability and a gre

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::c11916596277a37c6e12970a40be44a9
https://hal.archives-ouvertes.fr/hal-03227089/file/egc_braud-etal.pdf

Supervised quality evaluation of binary partition trees for object segmentation

Autor: Jimmy Francky Randrianasoa, Eric Desjardin, Nicolas Passat, Camille Kurtz, Nathalie Bednarek, François Rousseau, Pierre Gançarski, Pierre Cettour-Janet

Publikováno v: Pattern Recognition
Pattern Recognition, 2021, 111, pp.107667. ⟨10.1016/j.patcog.2020.107667⟩
Pattern Recognition, Elsevier, 2021, 111, pp.107667. ⟨10.1016/j.patcog.2020.107667⟩

International audience; The binary partition tree (BPT) allows for the hierarchical representation of images in a multiscale way, by providing a tree of nodes corresponding to image regions. In particular, cuts of a BPT can be interpreted as segmenta

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c5cf51cf5c8afa4f0298b80e906afcb6
https://hal.science/hal-02151830/document