Contact: Sébastien Angélliaume, Chengfang Ren, Jean-Philippe Ovarlez
firstname.lastname@example.org, email@example.com, firstname.lastname@example.org
M2 internship in the SONDRA laboratory at Centralesupelec and at ONERA
Starting date: between January and April 2021
Duration: 5 to 6 months
To apply, send a CV and a short description of your motivation to the supervisors
A doctoral thesis can be continued after this internship.
Keywords: Anomaly Detection, Robust detection, Synthetic Aperture Radar (SAR)
Voir également le fichier pdf StageM2AnomalyDetection
Anomaly detection aims to discover abnormal patterns hidden in multidimensional radar signals and images.
This research field is essential in data mining for quickly isolating irregular or suspicious segments in large amounts
of the database. Some examples are given below: a) Oil Slick, b) Turbulent ship wake, c) Levee anomaly, d)
Archeology. Among them, the unsupervised methods are the most interesting since they are widely applicable and do not require to label the
In this framework, we are focused on statistical detection, distance-based and density-based methods that
constitute the main families of anomaly detection methods. The latter already has remarkable success for detecting
anomalies in biological data [3, 4], hyperspectral data [5, 6, 7, 8, 9], etc. However, none of them is well adapted
for SAR imaging applications since (i) the diversity of SAR images (i.e. the number of channels, frequency bands,
etc.) is reduced compared to hyperspectral images rendering the extraction process of features limited. (ii) The
noise distribution is generally heavy-tailed, notably for high-resolution SAR images. (iii) The signal to noise ratio
is weaker compared to optical images. Therefore, the discrimination between a real anomaly and the noise is more
challenging than for RGB images. Note that there is some marginal tentative in the literature [10, 11].
In this thesis project, we propose to design new schemes tackling these issues and limitations. More specifically,
the goal of this thesis is to develop efficient and robust methods to extract abnormal ROIs in SAR images. We
firstly propose to handle heavy-tailed noise with the family of elliptical distributions [12, 13], which better fit the
noise distribution. Secondly, statistical and pixel-wise distances will be improved by using a natural Riemannian
metric rather than the standard Euclidean metric for better detecting abnormal ROIs. Under the availability of
labeled data, metric learning approaches  are also under the scope. Additionally, whitening and denoising
preprocessing steps are going under investigation to mitigate noise power. Finally, the developed methods will
1be applied for detecting anomalies in SAR images obtained from SETHI, Sentinel-1, UAVSAR and TerraSAR-X
missions. Notably, these proposed methods could be used for monitoring intrusion in vegetation zones.
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and Remote Sensing, 9(2):720–731, 2015.
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Advances in Multi-Sensor Adaptive Processing (CAMSAP), pages 169–172. IEEE, 2015.
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application to hyperspectral anomaly detection. In 2017 IEEE 7th International Workshop on Computational
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Tourneret. Detecting anomalous crop development with multispectral and sar time series using unsupervised
outlier detection at the parcel-level: application to wheat and rapeseed crops. 2020.
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performance of complex m-estimators for multivariate location and scatter estimation. IEEE Signal Processing
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