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.

Demo – Water detection on SWOT images

Last week, I participated at the “Journée Futur & Ruptures” organized by the fondation which finances half of my PhD (the other half being financed by the CNES).

This year, I was asked to prepare a demo. I did some kind of an interactive class that leads to the model I have been working on in the past months. The goal is to detect water in SWOT images.

You can find it here. Do not hesitate to contact me if you have remarks or if you find a bug.

Graph cut as an optimization technique

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.

First poster!

A while ago, I presented my first poster at the Colloque “Ressources naturelles et environnement” of Institut Mines-Télécom (which would be translated as “seminar on natural resources and environment”).

This poster quickly introduces my PhD subject, and I thought you may want to check it out. Unfortunatly, it is in french. So I will write an introduction to my subject in this blog anytime soon.

Here it is: (if the viewer do not work, please click here)