On Mon, Jan 3, 2011 at 5:28 PM, Roger Bivand <Roger.Bivand@nhh.no> wrote:

> On Mon, 3 Jan 2011, Chethan S wrote:
>
>  Hello everyone!
>>
>> I am using GRASS with spgrass6 for my work. I will be using variograms in
>> the process of landsat image classification. I am quite ok with GRASS but
>> am
>> finding R really tough. I understand that spgrass6 is a link between GRASS
>> and R which can read and write raster/vector layers. Out of really many
>> packages in R, for generating variograms out of landsat images which
>> packages of R can be used? I want to go for image segmentation after that
>> -
>> i.e., for further work I will be using object oriented classification.
>>
>
> You broke several basic list rules in this posting. You cross-posted to
> R-help - never cross-post, simply post on one list and wait for replies. If
> this doesn't satisfy you, or if so directed, post to another list.
> Cross-posting is bad because helpers on one list are probably not subscribed
> to others, so no-one can see all the replies to a thread as they are split
> among different lists. In addition, you stress that you are not willing to
> take the effort to try to find out for yourself - as a reply on R-help has
> already pointed out by suggesting a Google search.
>

My sincere apologies for cross posting. I just thought I could compare the
answers provided.

>
> Had you taken the trouble to visit the Spatial task view on CRAN, you would
> have found plenty of links to R packages for geostatistics. However, how
> that might fit into classification of satellite images is a completely
> different, and arguably wrong-headed question. Variograms and kriging are
> used for interpolation, not classification. So your work flow is seriously
> muddled too.
>

I have gone through Spatial task view on CRAN. Again I thought of confirming
what package to use, in case if my choice goes wrong in future.

>
> You seem to want to resample your images to a different resolution using
> geostatistical methods (why?) before classifying the new images; possibly
> you want to fill in missing pixels in your images before classification
> rather than resampling, but you don't say this. An area in which
> geostatistical methods are used in image classification is in the analysis
> of image texture, but you do not mention this as your goal. You would anyway
> find it easier to use the GRASS module r.texture instead of geostatics, I
> think.
>

What you say is right. I will be using geostatistical methods in the
analysis of image texture. Also for filling missing pixels in Landsat 7 ETM+
SLC-Off images I may use geostatistics.

>
> In any case, from your spate of postings on the GRASS users list, you do
> seem to rely on other people to do your work for you, don't you? Your
> posting on GRASS-stats of 6 December is very like the above, and went
> unanswered, because you were asking for an example study to follow.
>

Definitely, I don't rely on others to do the hard work for me. As this is
the first time we are using GRASS/R at our institute I didn't want to go
wrong anywhere. So far similar dissertation works were almost done using
proprietary packages.

>
> You need to design your own study (with your supervisor?), and post again
> only once you have clear and specific questions showing where the functions
> in the packages you are using are causing problems.
>

I will take due care before posting queries next time.

Sorry again!

>
> Roger Bivand
> (list admin.)
>
>
>> Regards,
>>
>> Chethan S.
>>
>>        [[alternative HTML version deleted]]
>>
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>>
> --
> Roger Bivand
> Economic Geography Section, Department of Economics, Norwegian School of
> Economics and Business Administration, Helleveien 30, N-5045 Bergen,
> Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43
> e-mail: Roger.Bivand@nhh.no
>

Thanks and regards,

Chethan S.

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