Short revisit cycle and temporal sampling

There is a somewhat recent trend in the development of new remote sensing satellite systems toward short revisit cycle (that is a high temporal resolution). Future systems such as Venµs or the Sentinels will be providing images of a given site every 2 to 5 days.

One may wonder which real applications may need this high temporal resolution. Although there are some applications which need it, it is also true that most of the applications foreseen for these systems will work with fewer images per period of time.

However, there are several point to take into account when we think about temporal sampling. First of all, at least for optical systems, the cloud cover can make unusable a high percentage of the images which are acquired by the sensors. Second, even if the mean number of images per year for a given application may be low, some of the applications may need a higher temporal resolution during some periods of the year (example of the phenology of crops). And the fact is that flight mechanics make easier to keep the same orbit all over the life span of a satellite.

And last, but not least, many new applications can emerge when this kind of data is made available to scientists.

In the coming versions of the Orfeo Toolbox, specific tools for image time series will be made available in order to help the users of this kind of data to use them efficiently.

Open tools for modeling and simulation

I has been now nearly 2 months since I moved to a new job. My research subject has slightly evolved, from sub-meter resolution image information extraction (mainly object recognition for scene interpretation) towards high temporal resolution for environment applications. The change in the resolution dimension (from spatial to temporal) is the main re-orientation of the work, but if I have to be honest, this would not be challenging enough to justify leaving what I was doing before, the nice colleagues I had (though the new ones are also very nice), etc.

The main challenge of the new job is to dive in the world of physics and modelling. Although I did a lot of physical modelling and simulation in my PhD, this was 10 years ago and it was SAR over the ocean, while now it is mainly optical over continental surfaces.

I have the chance to have landed on a lab with people who are specialists of these topics, so I can ask lots of questions about very specific issues. The problem is that I am lacking basic knowledge about state of the art approaches and I don't like to bother my new colleagues (since they are nice!) with stupid questions.

There is where open available ressources are very helpful. I will just cite 2 pointers among the lots of relevant stuff I am using for my learning.

In terms of modelling the physical and biological processes in an agricultural field, I found Daisy, which is an open source simulation system developed by Søren Hansen's team at the University of Copenhagen. Added to the source code, there is a very rich theoretical documentation available.

Once these processes are understood and simulated, I was also interested in learning how things can be measured by optical sensors. I found Stephane Jacquemoud's PROSAIL approach for which source code and reference documentation (papers, PhD dissertations) are available online.

From there, I just put things together in order to learn with a hands on approach. PROSAIL is Fortran and Daisy is C++. I wanted to plot some simulated vegetation spectra. So I fired up Emacs and started writing some python code in order to loop over variable ranges, launch the simulators, plot things with Gnuplot.py, and so on. And then, I remembered that we have python wrappers for OTB, which would make possible the use of the 6S radiative transfer code using OTB's internal version of it.

And here we are, with a fully open source system which allows to do pysics-based remote sensing. Isn't that cool?

I has been now nearly 2 months since I moved to a new job. My research subject has slightly evolved, from sub-meter resolution image information extraction (mainly object recognition for scene interpretation) towards high temporal resolution for environment applications. The change in the resolution dimension (from spatial to temporal) is the main re-orientation of the work, but if I have to be honest, this would not be challenging enough to justify leaving what I was doing before, the nice colleagues I had (though the new ones are also very nice), etc.

The main challenge of the new job is to dive in the world of physics and modelling. Although I did a lot of physical modelling and simulation in my PhD, this was 10 years ago and it was SAR over the ocean, while now it is mainly optical over continental surfaces.

I have the chance to have landed on a lab with people who are specialists of these topics, so I can ask lots of questions about very specific issues. The problem is that I am lacking basic knowledge about state of the art approaches and I don't like to bother my new colleagues (since they are nice!) with stupid questions.

There is where open available ressources are very helpful. I will just cite 2 pointers among the lots of relevant stuff I am using for my learning.

In terms of modelling the physical and biological processes in an agricultural field, I found Daisy, which is an open source simulation system developed by Søren Hansen's team at the University of Copenhagen. Added to the source code, there is a very rich theoretical documentation available.

Once these processes are understood and simulated, I was also interested in learning how things can be measured by optical sensors. I found Stephane Jacquemoud's PROSAIL approach for which source code and reference documentation (papers, PhD dissertations) are available online.

From there, I just put things together in order to learn with a hands on approach. PROSAIL is Fortran and Daisy is C++. I wanted to plot some simulated vegetation spectra. So I fired up Emacs and started writing some python code in order to loop over variable ranges, launch the simulators, plot things with Gnuplot.py, and so on. And then, I remembered that we have python wrappers for OTB, which would make possible the use of the 6S radiative transfer code using OTB's internal version of it.

And here we are, with a fully open source system which allows to do pysics-based remote sensing. Isn't that cool?

Geospatial analysis and Object-Based image analysis

I was searching in the web about the use of PostGIS data bases for object based image analysis (OBIA) and Google sent me to the OTB Wiki to a page that I wrote 6 months ago (first hit for "postgis object based image analysis").

It seems that this is a subject for which no much work is ongoing. Julien Michel will be presenting some results about how to put together OBIA and geospatial analysis (+ active learning and some other cool things) in a workshop in Paris next May.

Changing places

Today is my last day at my current position. On the 1st of March I will be at my new position at CESBIO.

This is not a major change. I keep working for CNES in remote sensing image processing, but I will be more focussed on multi-temporal image series (preparing Venµs and Sentinel-2 data use). The applicative context of all this work will be the Land Use and Cover Change.

I am mainly interested in introducing physical prior knowledge in image analysis techniques.

Simulation in Remote Sensing

Remote sensing images are expensive to buy. Remote sensing sensors are very, very expensive to design and build. Therefore, it may be interesting to know, before investing any money in images or sensors, which are the capabilities of an existing or future sensor.

In the Spring of 2009, Germain Forestier was a visiting scientist at CNES and we worked on this subject.

We (well, it was actually him who did the work!) implemented a simple simulator which used several spectral data bases, a set of sensors' spectral responses and generated as output the spectra which would have been obtained for each material of the database by each of the sensors.

The, we just applied classification algorithms in order to assess the quality of the classification results for each sensor. This simulator did not integrate atmospheric effects or spatial resolution information, so the conclusions drawn can not be used as general truth. However, we could show interesting things such as for instance, that the better results obtained by Pleiades HR with respect to Quickbird are due to the different design of the Near Infra-Red band (full disclosure: I work at CNES, where Pleiades was designed).

The detailed results of this work were published last Summer: G. Forestier, J. Inglada, C. Wemmert, P. Gancarski, Mining spectral libraries to study sensors' discrimination ability, SPIE Europe Remote Sensing, Vol. 7478, 9 pages, Berlin, Germany, September 2009

After Germain's leave at CNES, we have continued to work a little bit on the simulator in order to make it more realistic. We have already included atmospheric effects and plan to go further by introducing spatial resolution simulation.

I am convinced that this is the way to go in order to cheaply assess the characteristics of a given sensor in terms of end-user needs and not only in temrs of system quality issues as for instance SNR.

Multi-temporal analysis at IGARSS 2010

Professor Lorenzo Bruzzone has been extremely kind with me by proposing to me the co-chair
position for an invited session at IGARSS 2010. The session is entitled "Change Detection ad Multitemporal Image Analysis".

Land use and cover change is a major topic in remote sensing image processing and, as you may be aware of, there are many coming space borne systems which will be dedicated to this kind of application: Venus, the Sentinel Programme, etc.

In this context, there is an increasing need for processing techniques which allow to exploit the richness of this kind of data. The classical approach for the use of multi-temporal remote sensing
image data has been the use data assimilation frameworks. That is, models of evolution in which data is used mainly as a means for checking the soundness of models' predictions. This approach gives more weight to the model, since the data is scarce.

As pointed out above, in the coming years the amount of available data will be important, and one might want to try other approaches where data takes over models. Of course, it would be wrong to blindly apply data analysis tools without using existing models, but the assimilation techniques may benefit from changes induced by the availability of more data.