Thanks guys!  Will check out both of these options.  Maybe a silly question 
as I haven't investigated the remote modules closely, but will they work in 
a windows environment or is Linux recommended?

Cheers!

On Friday, January 27, 2017 at 3:54:02 AM UTC-5, Jordi Inglada wrote:
>
> Hi Jake, 
>
> You may try to approach your problem more systematically using the feature 
> selection module which is part of OTB's remote modules 
>
> https://github.com/boussaffawalid/FeatureSelection 
>
> This will however need that you compile your own OTB in order to activate 
> this module. If you don't feel like doing that, but you are comfortable 
> with some scripting (bash, python), you can try to loop over different band 
> combinations and use a cross-validation approach to select the best 
> combinations of input features. 
>
> I hope this helps, 
>
> Jordi 
>
> Jake Shermeyer <[email protected] <javascript:>> wrote: 
> > 
> > Hi all, 
> > 
> > I'm presently using the Gradient Boosting Tree classifier as part of the 
> TrainImagesClassifier tool. I have a vector training dataset and a 
> multi-band raster that serves as my input. I've been successfully 
> outputting models and cal/val confusion matrices. I then use these matrices 
> as verification data 
> > to optimize my classifier, adjust parameters, and tweak the bands I use 
> as inputs. 
> > 
> > As part of this process, I've been adding and removing bands included in 
> my input raster and I would like to see which bands are contributing or are 
> weighted the most in the classification. 
> > 
> > For example the classifier makes decisions and groups pixels into 
> specific classes based on each band x% of the time: 
> > Band 1- 10% 
> > Band 2- 25% 
> > Band 3- 50% 
> > Band 4- 15% 
> > 
> > Having this information will allow me to remove less valuable bands and 
> ensure I'm using the strongest set of data that will be the most effective. 
> > 
> > Is this a possibility? Or is there a comparable work-around if not? 
> > 
> > Thanks 
> > 
> > -- 
>

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