I did an oral presentation at RFIA 2016 at the begining of the month.
RFIA is a french conference (held at Clermont-Ferrand this year) about pattern recognition and artificial intelligence. My presentation was on “Un modèle de décomposition pour la détection de changement dans les séries temporelles d’images RSO” and the proceedings can be found in the publication section of this website.
My paper on Multi-temporal SAR image decomposition into strong scatterers, background, and speckle have been accepted at JSTARS.
It will feature a multitemporal decomposition model, that is able to take into account the scatterers that are present in SAR urban images. The model allows for the use of the L0 pseudo-norm for the detection of the scatterers and a prior using the Total Variation (TV) on the image. The optimum solution can be found exactly thanks to Graph-cut optimization. We also show two applications of the model: one for change detection, the other for regularization.
A draft of this paper will be soon available in the publications section.
Last week, I went to EUSAR 2016 in Hamburg to present a method for the classification of water in SWOT images.
This method tries to take into account the variations of the class parameters due to the antenna pattern that cannot be correctly compensated due to the absence of signal in one of the classes. Therefore, we needed to develop a binary classification method, where the parameters are not constant.
You can find the slides here and the article here.
I have recently been notified that my paper about TV+L0 decomposition on multi-temporal series of SAR images is accepted to MultiTemp2015.
SAR signals are different from classical (such as optic) ones because it contains speckle and strong scatterers. This implies that we can not obtain good results with traditional classical image processing techniques on SAR images. In this paper we introduce a regularization that suits multi-temporal series of SAR images combining total variation (TV) and a pseudo-norm L0 regularization.
This model is then optimized using a graphcut technique allowing us to find the global optimum. An application of this result for change detection is presented.
You can download the paper in the Publication section!
Back when I was studying at UPMC, I was asked to wrote a survey on optimization technique using graph cuts. This survey focus on certain problems expressed as Markov Random Fields (MRF) and show different algorithms that you can choose from depending on the number of classes in your problem, the regularity term of your model and the complexity.
Unfortunately, it is only available in French. You can find it here.