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.

Reproducible research

I have recently implemented the Spectral Rule Based Landsat TM image classifier described in:

Baraldi et al. 2006, "Automatic Spectral Rule-Based PreliminaryMapping of Calibrated Landsat TM and ETM+ Images", IEEE Trans. on Geoscience and Remote Sensing, vol 44, no 9.

This paper proposes a set of radiometric combinations, thresholds and logic rules to distinguish more than 40 spectral categories on Landsat images. My implementation is available in the development version of the Orfeo Toolbox and should be included in the next release:

• Landsat Indices
  
• Landsat Classifier
  

One interesting aspect of the paper is that all the information needed for the implementation of the method is given: every single value for thresholds, indexes, etc. is written down in the paper. This was really useful for me, since I was able to code the whole system without getting stuck on unclear things or hidden parameters.

This is so rarely found in image processing literature that I thought it was worth to post about it. But this is not all.

Once my implementation was done, I was very happy to get some Landsat classifications, but I was not able to decide whether the results were correct or not. Since the author of the paper seemed to want his system to be used and gave all details for the implementation, I thought I would ask him for help for the validation. So I sent an e-mail to A. Baraldi (whom I had already met before) and asked for some validation data (input and output images generated by his own implementation).

I got something better than only images. He was kind enough to send me the source code of the very same version of the software which was used for the paper – the system continues to be enhanced and the current version seems to be far better than the one published.

So now I have all what is needed for reproducible research:

1. A clear description of the procedure with all the details needed
   for the implementation.
   
2. Data in order to run the experiments.
   
3. The source code so that errors can be found and corrected.
   

I want to publicly thank A. Baraldi for his kindness and I hope that this way of doing science will continue to grow.

If you want to know more about reproducible research, check this site.