I just came back from IGARSS 17 which took place at Fort Worth, Texas, USA. I did two oral presentations on algorithms dedicated to Water detection in SWOT images:
- The first one is dedicated to the detection of large water bodies and uses two Markov Random Fields iteratively: the first one do the actual classification, while the second ones estimates the parameters of the class distributions.
- The other algorithm detects thin water surfaces (such as rivers). It combines a pixel-based detector (which detects parts of the river network) and a higher-level step connecting detected segments.
More information can be found in the proceedings posted in the publication page.
Last october, I participated to the “Journées CNES Jeunes Chercheurs 2016” (JC2
), where I had the chance to give a short talk and present a poster about the work I have done so far at my PhD. It was a great opportunity for me to present my work to talented scientists from very different fields!
As this poster sum up what I have been up to, I thought it would be nice to put it here.
Unfortunately, it is in French… If you only speak english (or want more details!), feel free to take a look at the updated publiactions page!
Here it is: (if the viewer do not work, please click here
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.