[R-sig-Geo] image classification in R

[Agustin Lobo]

Beware that results yield by hierarchical clustering are very dependent
upon the actual initial subsample,
Agus
Corey Sparks wrote:
> Yes, I've already run into this problem trying to run hclust on a 
> landsat image. I'm on an imac with leopard and 4gb of ram and the 
> distance matrix needed wouldn't fit into memory. I will try the 
> sub-sampling technique. Thank you all for you assistance.
> Corey
> Corey Sparks
> Assistant Professor
> Department of Demography and Organization Studies
> College of Public Policy
> One UTSA Circle
> San Antonio, TX 78239
> corey.sparks 'at' utsa.edu
>
>
>
>
> On Apr 18, 2009, at 6:54 AM, Agustin Lobo wrote:
>
>> In my opinion, and considering that imagery uses to be very large
>> datasets, unless you want to include spatial characteristics, the best
>> is to subsample your imagery with your gis, then import the
>> dataset to R, perform classification with the many tools
>> available, save the centroids (means, sds, covar matrices depending
>> on your method) and then allocate pixels to those centroids in your
>> gis.
>>
>> Images are too large for R
>>
>> Agus
>>
>> Edzer Pebesma wrote:
>>> There's a Task View on clustering, linked from CRAN:
>>>
>>> http://cran.r-project.org/web/views/Cluster.html
>>>
>>> that will lead you to all types of clustering available, including
>>> hierarchical. I don't know how well it will work for large data sets
>>> such as images, as it calls for constructing n x n distance matrices,
>>> with n the number of pixels.
>>> -- 
>>> Edzer
>>>
>>> Hengl, T. wrote:
>>>
>>>> Don't forget that you can also use different types of unsupervised 
>>>> classification methods, such as the fuzzy k-means as implemented in 
>>>> the "kmeans" method.
>>>>
>>>> Here is an example (with landform classes):
>>>> http://spatial-analyst.net/wiki/index.php?title=Analysis_of_DEMs_in_R%2BILWIS/SAGA 
>>>>
>>>>
>>>> If you work with large grids, consider also using R+SAGA:
>>>> https://stat.ethz.ch/pipermail/r-sig-geo/2009-February/005155.html
>>>>
>>>>
>>>> T. Hengl
>>>>
>>>>
>>>>
>>>> -----Original Message-----
>>>> From: r-sig-geo-bounces at stat.math.ethz.ch on behalf of Edzer Pebesma
>>>> Sent: Fri 4/17/2009 5:32 PM
>>>> To: Corey Sparks
>>>> Cc: r-sig-geo at stat.math.ethz.ch
>>>> Subject: Re: [R-sig-Geo] image classification in R
>>>> Corey,
>>>>
>>>> you can use functions lda or qda (in library MASS) for linear or
>>>> quadratic discriminant analysis, respectively, on your training/ground
>>>> truth data, and then use the predict method on the resulting objects,
>>>> passing the bands (you need to convert the SpatialGridDataFrame to a
>>>> data.frame) as newdata to obtain the classified pixels. Make sure that
>>>> the band names have identical name in both cases. Then assign the
>>>> predicted class to the SpatialGridDataFrame and export.
>>>>
>>>> It has never been clear to me whether "maximum likelihood
>>>> classification" in RS refers to lda or qda. Anyway, it's called
>>>> discriminant analysis in the statistical literature.
>>>> -- 
>>>> Edzer
>>>>
>>>>
>>>> Corey Sparks wrote:
>>>>
>>>>> Dear list,
>>>>> I want to do some unsupervised image classification of some landsat
>>>>> imagery, I think I can read in the multi-band rasters using rgdal, 
>>>>> but
>>>>> has anyone tried doing this in R? I am thinking (after looking at
>>>>> documentation for how GRASS and ArcGIS do it) that I need to do an
>>>>> initial hierarchical clustering to define clusters, but does anyone
>>>>> have an idea on how to do a maximum likelihood classification of the
>>>>> imagery? Would a discriminant function approach work? Any advice
>>>>> anyone may have would be greatly appreciated, and i'm very sorry 
>>>>> but I
>>>>> don't have a working example yet.
>>>>> Best
>>>>>
>>>>> Corey
>>>>>
>>>>> Corey Sparks
>>>>> Assistant Professor
>>>>> Department of Demography and Organization Studies
>>>>> University of Texas at San Antonio
>>>>> One UTSA Circle
>>>>> San Antonio, TX 78249
>>>>> 210 458 6858
>>>>> corey.sparks 'at' utsa.edu
>>>>>
>>>>> _______________________________________________
>>>>> R-sig-Geo mailing list
>>>>> R-sig-Geo at stat.math.ethz.ch
>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>>>>>
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>>>>
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>>>
>>>
>>
>> <alobolistas.vcf>
>
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