Analyse de séries chronologiques d'images satellitaires orientées objet à l'aide d'une représentation graphique
| Autor: | Maguelonne Teisseire, Lynda Khiali, Dino Ienco |
|---|---|
| Přispěvatelé: | Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ADVanced Analytics for data SciencE (ADVANSE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Exploit
Computer science 0211 other engineering and technologies DONNEE DE SATELLITE CLUSTERING 02 engineering and technology computer.software_genre remote sensing 0202 electrical engineering electronic engineering information engineering Segmentation Cluster analysis TELEDETECTION Ecology Evolution Behavior and Systematics satellite data 021101 geological & geomatics engineering Interpretability Object-oriented programming Ecology BASE DE DONNEES ORIENTEES OBJET Applied Mathematics Ecological Modeling Graph based Computer Science Applications Computational Theory and Mathematics Modeling and Simulation [SDE]Environmental Sciences Graph (abstract data type) 020201 artificial intelligence & image processing Satellite Image Time Series Data mining computer |
| Zdroj: | Ecological Informatics Ecological Informatics, Elsevier, 2018, 43, pp.52-64. ⟨10.1016/j.ecoinf.2017.11.003⟩ Ecological Informatics, Elsevier, 2018, pp.52-64 |
| ISSN: | 1574-9541 |
| DOI: | 10.1016/j.ecoinf.2017.11.003⟩ |
| Popis: | International audience; Nowadays, remote sensing technologies produce huge amounts of satellite images that can be helpful to monitor geographical areas over time. A satellite image time series (SITS) usually contains spatio-temporal phenomena that are complex and difficult to understand. Conceiving new data mining tools for SITS analysis is challenging since we need to simultaneously manage the spatial and the temporal dimensions at the same time. In this work, we propose a new clustering framework specifically designed for SITS data. Our method firstly detects spatio-temporal entities, then it characterizes their evolutions by mean of a graph-based representation, and finally it produces clusters of spatio-temporal entities sharing similar temporal behaviors. Unlike previous approaches, which mainly work at pixel-level, our framework exploits a purely object-based representation to perform the clustering task. Object-based analysis involves a segmentation step where segments (objects) are extracted from an image and constitute the element of analysis. We experimentally validate our method on two real world SITS datasets by comparing it with standard techniques employed in remote sensing analysis. We also use a qualitative analysis to highlight the interpretability of the results obtained. |
| Databáze: | OpenAIRE |
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