Offre en lien avec l’Action/le Réseau : – — –/– — –
Laboratoire/Entreprise : Mathématiques et Informatique Appliquées – UMR 518
Durée : 3 ans
Contact : email@example.com
Date limite de publication : 2021-09-15
Analysing heterogeneous remote sensing time series using supervised methods requires that the classes sought are perfectly known and defined and that the expert is able to provide a sufficient learning data set both in number and quality. Faced with the difficulty of obtaining sufficient examples within the context of the analysis of time series of remote sensing images, we propose to develop an innovative method of interactive multi-paradigm collaborative learning. The aim is to enable the expert to add “on the fly” information (labels, constraints, etc.) used to guide the learning process in order to produce clusters and models closer to the expert’s “intuitions”, i.e. potential thematic classes. To do this, the expert will be actively assisted by the system, which will for example offer advice or proposals for new constraints or labelling of objects. We will validate our work in several fields of application chosen in agreement with partners of the HERELLES project.
With the launch and entry into production of the European satellites in the Sentinel or Franco-Israeli constellation Venųs, satellite data are now arriving in massive, almost continuous flows. This massive influx of temporal data should lead to major advances in various Earth and environmental science disciplines for the study and modelling of complex phenomena (agricultural or urban dynamics, deforestation, anthropogenic actions on biodiversity, etc.). However, faced with this overabundance of temporal data, arriving almost continuously, the labelling phase of supervised learning can no longer be carried out by experts, as it is too tedious and time-consuming. Moreover, the supervised learning methods classically used in Earth observation assume that the learning data sufficiently and completely describe the classes to which they are attached. In other words, these methods require that the desired classes are well known and defined and that the expert is able to provide a sufficient set of learning data both in number and quality. In the case of temporal analysis in remote sensing, this assumption is no longer realistic. Indeed, the technological revolution of high-frequency image acquisition is still too recent for thematic knowledge to have adapted. Thus, there are currently no typologies (or nomenclatures) of changes that can really be used for this type of supervised analysis and therefore no associated quality learning data.
To compensate for this lack of formalization and examples, the expert must be able to rely on other types of information such as partially labelled data, formalized knowledge, constraints on data or results. At the same time, there are also numerous methods capable of analyzing this data. Combining these data and methods seems indispensable. Thus, approaches such as boosting, clustering or collaborative clustering take advantage of the complementarity between different methods, each with its own biases and its own analysis strategy but capable of processing its own data in a privileged way.
However, with the increase in the volume of data and the number of potential evolution classes, the highlighting and formalization of information that is really relevant for classification methods in the context of temporal analysis appears to be more difficult than expected and potentially time-consuming. The objective of this project, in strong link with the ANR HIATUS and HERELLES projects, is to define and validate in the context of high acquisition frequency remote sensing, an innovative method of interactive collaborative learning. The aim is to enable the expert to add “on the fly” information (labels, classes, constraints, etc.) that can be used to guide the learning process in order to produce clusters and models that are closer to the expert’s “intuition”, i.e. potential thematic classes . To do this, the expert will be actively assisted by the system, which will offer advice or proposals for new constraints or object labelling, for example.
Profil du candidat :
Master’s Degree in Computer Science or equivalent.
Formation et compétences requises :
The candidate must have good skills in data analysis and more particularly in supervised or unsupervised classification of time series. Skills in remote sensing image analysis is required. Good knowledge of English (French is not mandatory)
Adresse d’emploi :
Paris Campus Saclay
Document attaché : 202107060833_Sujet_HERELLES_2021.pdf