Moving the blog

I am in the phase of moving my blogs to 

http://blog.jordiinglada.net/

PROBA-V Preparatory Programme - PRO-Fusion

PROBA-V stands for PRoject for On-Board Autonomy - VEGETATION and is a space mission aimed at ensuring the data continuity between the Végétation missions (on board of CNES' SPOT-4 and SPOT-5) and ESA's Sentinel-3.

PROBA-V has a Preparatory Programme funded by Belspo, the Belgian Federal Scientific Policy Office. I am PI for one of the selected projects of this programme. As such, I participated at the end of February to one of the International Users Committee to present the goals of the project.

The project is called PRO-Fusion and it aims at developing and assessing algorithms for the fusion of PROBA-V images with other types of images as for instance Venus or Sentinel-2.

The slides of my presentation are available here.

One interesting thing of this project is that the source code of the algorithms will be available as open source software. I have already started to contribute some code to the Orfeo Toolbox. The main contributions involve the multi-temporal processing.

HDF4 support for GDAL on Arch Linux

I have been trouble reading HDF files with OTB on Arch Linux for a while. I finally took the time to investigate this problem and come to a solution.
At the beginning I was misled by the fact that I was able to open a HDF5 file with Monteverdi on Arch. But I finally understood that the GDAL pre-compiled package for Arch was only missing HDF4 support.
It is due to the fact that HDF4 depends on libjpeg version 6, and is incompatible with the standard current version of libjpeg on Arch.
So the solution is to install libjpeg6 and HDF4 from the AUR and then regenerate the gdal package, who, during the configuration phase will automatically add HDF4 support.
Here are the detailed steps I took:

1. Install libjpeg6 from AUR:
   1. mkdir ~/local/packages
   2. cd ~/local/packages
   3. wget http://aur.archlinux.org/packages/libjpeg6/libjpeg6.tar.gz
   4. tar xzf libjpeg6.tar.gz
   5. cd libjpeg6
   6. makepkg -i #will prompt for root psswd for installation
2. Install HDF4 from AUR:
   1. cd ~/local/packages
   2. wget http://aur.archlinux.org/packages/hdf4-nonetcdf/hdf4-nonetcdf.tar.gz
   3. tar xzf hdf4-nonetcdf.tar.gz
   4. cd hdf4-nonetcdf
   5. makepkg -i #will prompt for root psswd for installation
3. Setup an Arch Build System build tree
   1. sudo pacman -S abs
   2. sudo abs
   3. mkdir ~/local/abs
4. Compile gdal
   1. cp -r /var/abs/community/gdal ~/local/abs
   2. makepkg -s #generated the package without installing it
   3. sudo makepkg -U gdal-1.8.0-2-x86₆₄.pkg.tar.gz

If you are new to using AUR and makepkg, please note that the AUR package installation need the sudo package to be installed first (the packages are build as a non root user and sudo is called by makepkg).
Step 3 above is only needed if you have never set up the Arch Build System on your system.

Understanding map projections

Map projections are both easy and tricky. I am not a specialist at all about the subject, but I have used them a little bit.

When I say that map projections are easy, I mean that, even without understanding them completely, there are tools which allow a user to get the work one. Of course, I use the Orfeo Toolbox library, which in turn uses the OSSIM library. Actually, some years ago, with a student, we designed the interface in order to integrate OSSIM map projections (and sensor models too) into OTB so that they could be used as the existing geometric transforms already available in OTB (which came from ITK).

The only thing we had to understand in order to design this interface was that map projections are coordinate transformations. Then, we chose the appropriate object-oriented design pattern together with a bit of C++ templates for the generic programming ingredient and that was it.

When some users started using the projections (well, WE were those users) questions arised about the projections themselves. The task in answering to these questions would have been much easier if we had had this info (posted by Caitlin Dempsey on GIS Lounge):

/"The USGS has posted a scanned file of John P. Snyder's 1987 "Map/ Projections: A Working Manual" online in PDF and DjVu format. The beginning of the book contains substantial introductory information about map projection families and distortions. Each projection is started with a useful summary of the context and usage of that particular projection. Snyder then delves into detail about the history, features, and usage before providing the mathematical /formulas used to calculate the projection."/

Traffic Monitoring with TerraSAR-X

I have just read this interesting article which describes away to measure vehicle speeds using space-borne SAR sensors. The article explains very clearly how Doppler effect can be used, either with one single image, or with an interferometric pair, to estimate the speed of cars or ships.

DLR's TerraSAR-X system has always impressed me. It has been providing very high quality images from its launch. For having used the images, I can say that their geometrical quality is very good. And as far as I know, this is the only operational system in flight which is able to perform one-pass interferometry.

So far so good.

However, the article forgets to mention that these satellites acquire only 2 images per day on a given point of the Earth's surface -- typically in the morning and in the evening – so one can not expect to use this technology for real-time traffic monitoring.

So you don't need to worry about getting a ticket for speed.

Author: Jordi Inglada

Teenager contribution for remote sensig

Check out this article. A teenager proposes an instrument concept for measuring the temperature at the bottom of clouds. NOAA is testing it.

Multi-temporal series simulations

As I mentioned in a previous post, last September I participated to the Recent Advances in Quantitative Remotes Sensing Symposium. I presented several posters there. One of them was about the assessment of the classification accuracy of Venus and Sentinel-2 sensors for land cover map production.

While the results of the study are interesting, I think that the mostimportant thing that this paper shows is how a time series withrealistic reflectance values can be simulated.

The idea here is to find a good balance between image synthesis (low accuracy) and physically sound simulation (need for ancillary data and computational complexity). The choice made here is to use a real time series of Formosat-2 images (only 4 spectral bands) in order to simulated Venus and Sentinel-2 time series with the same temporal sampling but with more than 10 spectral bands.

The Formosat-2 time series is used in order to:

1. Estimate the LAI (leaf area index) for each pixel
   
2. Give a spatial distribution using a land-cover map
   

A database containing leaf pigment values for different types of vegetation is used together with the above-mentioned LAI estimation in order to drive a reflectance simulator. The simulated reflectances are then convolved with the relative spectral responses of the sensors in order to generate the simulated images.

The poster presented at RAQRS 2010 is here.