Hello, I'm really new using Monteverdi, but I need to use ir for the final 
project in my studies.
I want to do the SVM Classification with SHP files from Q-GIS, but I don't 
know how to do it in windows and I don't know how to program, I was using 
it in Ubuntu but is very different in Windows, I hope you can give an idea.

Thanks!

Relder

El martes, 28 de enero de 2014 15:17:46 UTC+1, Charles Peyrega escribió:
>
>  Hi Saygin.
>
> Let us consider your smallest class, the size of which will limit the 
> maximal number of training/validation pixels. You can write:
>
> Nb_Pixels_Smallest_Class = 115 pixels
> Ratio = 0.5
>
> Then, for EACH class, your training is made over about (0.5 x 115) = 57.5 
> pixels. Moreover, you have 4 classes, then you will train your model over a 
> total number of (4 x 57.5) = 230 pixels, which approximately corresponds to 
> your 
> actual number of 232 training samples. Your validation data set is 
> composed of 184 pixels corresponding to about (184./4) = 46 pixels for EACH 
> class, which is quite close of the 57.5 pixels value of the training set. 
> In fact both values are not strictly identical because the actual number of 
> pixels over which the random sample selection is made (115 pixels) is too 
> small to obtain similar values. However, you can consider that they are 
> close enough to each other.
>
> Please find below another example I gave as an answer over the otb-users 
> mailing list concerning a similar issue but concerning both "sample.mt" 
> and "sample.mv" parameters of the OTB Application 
> otbapp_TrainImagesClassifier, which also have an infmuence of the actual 
> number of samples randomly selected:
>
> Considering Nb_Pixels_Smallest_Class = 430 pixels. Let us consider now an 
> unbalanced ratio R = 0.1, (90% is used for Training and 10% for 
> Validation). You will randomly select about 387 samples for T and 43 for V 
> in EACH of your nbC classes if "sample.mt" = "sample.mv" = (-1).
>
> If Ratio=0.1: (90% is used for T and 10% for V):
>
> If "sample.mt" = 100 < 430*0.9, you will use about 100 samples for T for 
> each of your nbC classes.
> If "sample.mv" = 100 > 430*0.1, you will use about 43 samples for V for 
> each of your nbC classes.
>
> Then, to sum up, if you want to use the maximal available number of 
> samples per class for T and V, you simply need to choose "sample.mt" = "
> sample.mv" = (-1). However, this maximal number of samples per class will 
> be limited by the size of your smallest class. Then, you can set the 
> ratio R according to your needs.
>
>
> Best regards.
>
> Charles Peyrega
>
>
>
> Le 28/01/2014 12:43, saygin a écrit :
>  
>
> Hello, 
>
>  I am trying to train my .tif image with approximately 1500 pixels. I 
> have 4 classes and one of them has the lowest training with 115 pixels. If 
> I chose training and validation sample ratio 0.5, and  using SVM OpenCV RBF 
> with parameter optimization I get 232 training and 184 validation at log 
> file. I am not very familiar with OTB and want to ask does not it 
> use approximately half of all training pixels? 
>
>  Thanks
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> -- 
>   <http://www.c-s.fr>  *Charles PEYREGA*, *PhD*
> Ingénieur d'études métier
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