Offre en lien avec l’Action/le Réseau : – — –/– — –
Laboratoire/Entreprise : IRISA
Durée : 3 ans
Contact : email@example.com
Date limite de publication : 2022-03-25
On March 7, 2017, the European Space Agency (ESA) successfully put its latest high-resolution satellite Sentinel-2B into orbit. The two Sentinel-2 satellites are now capturing images of all emerged surfaces every 2 to 5 days at high spatial and spectral resolutions, which makes it possible to monitor the evolution of land surfaces on a global scale. Satellite image
time series (SITS) extracted from Sentinel-2 constellations are useful for many applications such as land cover mapping , crop type mapping, soil moisture estimation, burnt area detection, and many more. Similarly, high resolution satellite image time series, such as the one provided by Pl ́eiades, are relevant for urban area analysis, disaster risk management,
and rapid mapping.
Due to their volume and complexity, the analysis of these spatio-spectro-temporal datacubes requires automatic tools. Recent advances have been marked by the use of deep learning techniques to make the most of the temporal structure of SITS. It includes temporal 1D convolutions , recurrent networks , or attention-based architectures . There were also several attempts to jointly exploit the spatial and temporal dimensions of SITS data by the means of deep learning [5, 6, 7]. Although these approaches have proven their efficiency, they suffer from two main issues: (i) they require a vast amount of high-quality labelled data, and (ii) they ignore previous trends in remote sensing, especially contributions from
the object-based image analysis (OBIA) . Ensuring a convergence between these two distinct paradigms would allow embedding more structural and semantic information in the process. Preliminary attempts to couple the two paradigms need to be pursued .
Since only a few attempts exist to jointly use temporal relationships between satellite images and their intrinsic spatial structure in deep learning, the Ph.D. aims at developing novel deep learning architectures for the generation of spatio-temporal objects with no or limited supervision. It will be composed of two main objectives: (i) developing new techniques to
structure raw SITS data into spatio-temporal objects, and (ii) analysing spatio-temporal objects.
First, we will consider the task of extracting objects from SITS as either a temporal sequence of 2D objects or directly 3D (i.e., spatio-temporal) objects with no or a few supervision. For this task, we will propose new unsupervised deep learning strategies that take inspiration from self-supervised strategies  and go much further than the segmentation networks introduced recently in the literature, e.g. based on auto-encoders such as W-Net , or having new loss formulation . We will also consider the case where a weak reference is available as we know that prior knowledge can be used to guide the extraction of objects. In our setting, this prior knowledge can come from sparse, single-date, and low-resolution
reference data (e.g., Corine Land Cover) or from the application of a ruleset, applied to each image of the SITS, which enriches the objects with some semantics. Compared to computer vision algorithms used for instance segmentation such as Mask-RCNN, the novelty will be to take into account both spatial and temporal structures of SITS data, to deal with the lack of quality labelled reference data at the object level, and to provide a full partition of the input data.
Second, we will develop new methods to analyze the produced spatio-temporal objects. We will still consider the deep learning framework as a methodology to perform object-based time series analysis. To do so, we will represent objects as nodes in a spatio-temporal graph, such as Graph CNNs  and their formulation in the spatio-temporal domain . This representation will be then used for classical applications such as land cover mapping. We will also propose and evaluate similarity measures between nodes in the graph representation to cluster the data.
Profil du candidat :
We are looking for a candidate with strong data analysis, machine learning, and image processing skills, who is familiar with deep learning techniques.
Formation et compétences requises :
The candidate should have excellent programming skills in at least one language (Python, C/C++, etc.). Knowledge of time series analysis and remote sensing techniques will be appreciated. Good communication skills (at least in English) are required.
Adresse d’emploi :
Université Bretagne Sud
Campus de Tohannic
56 000 Vannes
Document attaché : 202112171454__CNES__Ph_D__offer.pdf