23-168 Towards Modern Change Detection for Earth
il y a 2 semaines
Doctorat, 36 mois
- Temps plein
- Moins de 2 ans d’expérience
- Master, DESS, DEA, Bac+5
- Digital technologies for remote sensing
**Mission**:
Earth Observation (EO) data are now abundant thanks to Sentinel-2, SPOT-6/7 or Pleiades constellations but also to national aerial photo campaigns. Automatic EO analysis is key for many public policies for monitoring climate change or biodiversity erosion. Indeed, the annotation of satellite or aerial images is a long and costly task that must be continually renewed. Large geodatabases describing the territory have been created by institutions such as IGN-France. Yet, the main question is now how to efficiently and swiftly update them. The main task is to be able to detect a meaningful difference between an information present in a geographic database and a new image acquisition. This thesis proposal will therefore focus on the challenging task of change detection in remote sensing images with modern machine learning. It is grounded on operational problems arising from land monitoring, in connection with the updating of the IGN databases and leveraging open very high resolution geospatial imagery. However, current EO machine learning techniques are almost always faced with a lack of available labelled data, requiring novel directions.
Objectives
Two key solutions are proposed to tackle this problem: synthetic data creation and self-supervised pre-training adressing two parallel challenges: geodatabase updating and comprehensive change detection.
Synthetic training dataset This first direction consists in producing a methodology to create synthetic but relevant training datasets for the change detection task. There is currently no publicly available and sufficiently massive dataset for addressing such a problem. To avoid manually labelling the changes, we target to propose a procedure to exploit existing large-scale image datasets already labelled such as IGN geodatabases. The advantage of considering such high precision databases is that they may contain instance segmentation of objects of interest: we therefore propose to create synthetic datasets by deleting or adding objects (a building, a tree, a road, a river) into images. We will rely on inpainting and seamless image editing methods to simulate visually realistic changes (to be compared with radiative transfer based solutions). This research track requires to define possible and feasible transformations between pairs of synthetic images. These geometric and colorimetric transformations have to correspond to the invariant we want to inject into the model. They can come from physical or learned models. This approach would allow to create open synthetic datasets containing hundreds of thousands of aligned images with corresponding change map, that will be used to train change detection model based on Convolutional Neural Networks or Transformers.
Database updating:
Most Very High Resolution change detection methods consist in detecting changes between two remote sensing images. However, one of the important task is upgrading available geodatabases. Our objective is to develop a new deep architecture that both outputs a change map between the geographic database and a new snapshot of the area. This allows to directly update the database which could be already partially updated or wrong, instead of traditional sequential approaches with final tedious topological editions. The main difficulty stem from the fact that the geographic data is an abstract representation of the world. Our model will have to be agnostic to the type of input images but also to the label considered.
Pre-training on unlabelled data:
Another research direction focuses on the transfer of models pre-trained on unlabelled data. The self-supervised pre-training methods mainly focus on classification; our aim is to find pre-training tasks that are efficient for change detection. Indeed, some works have started to study the implementation of "pretext tasks" that are specific to the domain of remote sensing such as temporal or georeferencing but never for change detection. Once the model has been pre-trained with the help of a self-supervised learning method, it can be transferred to the change detection task by fine-tuning or by using a semi-supervised framework.
Comprehensive Change Detection:
Currently, change detection methods focus on a particular type of change, e.g., urban or littoral use cases. It is a necessity to be able to perform a comprehensive change detection for each pixel of a pair of two images.
A model should predict the origin of the change observed between each of the pixels, whatever its type: radiometric, seasonal, abrupt or long-term anthropic modification. In order to be able to learn this type of information, it is essential to work with several image modalities and even satellite time series. The use of Bayesian method could allow to manage this set of cumulative causes of change.
The methods developed in this th
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