[GRASS-user] High-resolution agricultural land cover from satellite imagery

[Luigi Ponti]

Dear Markus

On 16/05/2012 14:19, Markus Neteler wrote:
> ...
> You may use neural networks in R (using the GRASS-R interface).
> For a general overview of the integration, see
> http://grass.osgeo.org/wiki/R_statistics

Right. Sometimes I forget the secret weapon...

> ...
> As my master thesis, I wrote these two modules:
>
> i.spec.sam: Spectral Angle mapping
> http://grass.osgeo.org/wiki/Addons#i.spec.sam
>
> i.spec.unmix: Spectral unmixing
> http://grass.osgeo.org/wiki/Addons#i.spec.unmix
>
> The latter needs proper update to GRASS 6 but that should not be too hard.
>

I had a feeling I was missing something important.

In the description of the two add-on modules, I found reference to two 
very interesting papers that triggered some more search. In particular, 
this conference paper by Stabile et al. (2009) on Fusion of 
High-resolution Aerial Orthophoto with LandSat TM Image for Improved 
Object-based Land-use Classification, uses the same data layers I may 
access (orthophoto + Landsat TM):
http://www.a-a-r-s.org/acrs/proceeding/ACRS2009/Papers/Oral%20Presentation/TS12-05.pdf

based on which, I wonder if it would make any sense to perform i.fusion e.g.

i.fusion.brovey -l ms1=lsat7_2002_20 ms2=lsat7_2002_40 \
                    ms3=lsat7_2002_50 pan=ortho_photo_rgb_composite \
                    outputprefix=brovey

and then use i.smap after i.gensigset (the use of group= and subgroup= 
parameters in both modules is a bit unclear to me) to classify the map 
using areas with known land cover class identified via the orthophoto 
itself or field survey.

Kind regards and thank you (and pardon my ignorance),

Luigi