[R-sig-Geo] Classification of attribute table

[Dylan Beaudette]

See the clara() function from the cluster package. It scales fairly
well to larger-sizes data sets.

Cheers,
Dylan

On Mon, May 11, 2009 at 5:35 AM, Wesley Roberts <wroberts at csir.co.za> wrote:
> Hi Dan,
>
> Thanks for the advice. I want to classify my data into three classes; canopy, non-canopy and ground based on six input variables. The input variables are mean, min, max, median, var, stdev, and kurtosis of spatially co-incident spectra associated with each segment. I have 1916 cases and the data are formatted like an ESRI attribute table, each row corresponds to one particular segment,
>      mean  min  max  median  var  stdev  kurtosis
> 1
> 2        values extracted from the imagery
> 3
> .
> .1916
>
> I would thus like to classify the segments into three classes and essentially add an additional column to the attribute table with values 1, 2, and 3 denoting the class of the particular segment. Ideally the classification must be un-supervised as the whole procedure should be as automatic as possible with limited input from the user. Initially I wanted to use lda (MASS) but it required training classes.
>
> An alternative option is to use the hypothesis that segments with brighter spectra are more likely to come from tree crowns and thus just subset / select the segments which fall into for example the 90th percentile and label those as tree crowns.
>
> Many thanks,
> Wesley
>
>
>
> Wesley Roberts MSc.
> Researcher: Earth Observation (Ecosystems)
> Natural Resources and the Environment
> CSIR
> Tel: +27 (21) 888-2490
> Fax: +27 (21) 888-2693
>
> "To know the road ahead, ask those coming back."
> - Chinese proverb
>
>
>
>>>> Dan Putler <dan.putler at sauder.ubc.ca> 05/07/09 6:13 PM >>>
> Hi Wesley,
>
> Is this classification problem or a clustering problem? Specifically, is
> the ultimate goal to predict what segment a new polygon belongs in, or
> are you trying to form 3 segments to begin with based on the six
> measures you have available? If it is the latter, it is a cluster
> analysis problem rather than a classification problem, and you'll want
> to look at the Cluster Analysis and Finite Mixture Models task view at
> http://cran.r-project.org/web/views/Cluster.html.
>
> Dan
>
> On Thu, 2009-05-07 at 14:58 +0200, Wesley Roberts wrote:
>> Dear R-sig-geo users,
>>
>> I have the output of a watershed segmentation in vector format (shapefile) which has it's attribute table populated with statistics regarding spectral reflectance of each polygon object. The attribute data was sourced from a geographically co-incident aerial photograph. I would now like to classify the segments using the attribute data. This seems like an easy task but I am struggling to find a suitable method. I have looked at 'lda' and 'qda' in the MASS package but the selection of an appropriate model using 'cv1EMtrain' takes a really long time. In essence all I want to do is classify the 6 variable data set into 3 classes with the class for each case recorded in the attribute table.
>>
>> Any advice or suggestions would be greatly appreciated.
>>
>> Many thanks and kind regards,
>> Wesley
>>
>>
>>
>> Wesley Roberts MSc.
>> Researcher: Earth Observation (Ecosystems)
>> Natural Resources and the Environment
>> CSIR
>> Tel: +27 (21) 888-2490
>> Fax: +27 (21) 888-2693
>>
>> "To know the road ahead, ask those coming back."
>> - Chinese proverb
>>
>>
>>
>>
> --
> Dan Putler
> Sauder School of Business
> University of British Columbia
>
>
>
> --
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