Hi Guillaume,
I'm also trying to realize a supervised object-based classification.
I've realized all the steps of LSMS (Smoothing, segmentation, smallregion
merging and vectorization)
My shapefile contains mean and var data from B1,B2 B3 and B4 and also for
NDVI SAVi and NDWI
then I imported the shapefile in QGIS and added a "training" column.
I selected several polygons and attributed them a class.
Create a new shapefile containing only polygons with a class number
(samples.shp)
I'm trying now to define classes for each polygon of my segmentation based
on my samples shapefile.
What i'm a doing is :
1. PolygonClassStatistics --> polygon_stats.xml
2. TrainVectorclassifier --> obtaining a model.svm file and matrix.csv file
3. When I run OGRLayerClassifier
input shapefile = segmentation shapefile
xml file = polygon_stats.xml
input model filename = model.svm
Feature (mean and var of different bands)
It gave me a message error : ShiftScaleSampleListFilter(0000020CF323F6A0):
Inconsistent measurement vector size : input sample list size 14 sclae
measurement vector size 7 shift measurement vector size 7
Could you tell me what does it mean and how to solve the problem ?
Best regards
Daphné
Hi Guillaume,
I'm also trying to do supervised object-based classification.
What I did so far is a meanshift segmentation on my satellite raster image.
Then I did TrainImagesClassifier (svm) on the vector results of the
segmentation.
And at this point I'm not sure anymore what to do as there was no output on
the previous step.
Could you guide me from the segmentation to have an object-based
classification using SVM ?
(I tried to do OGRLayerClassification, but had no idea of what to put in
the xml inputs etc..., the only input I have are my raster and my
segmentation vector result)
Le mardi 3 janvier 2017 14:43:57 UTC+1, Guillaume Pasero a écrit :
>
> Hi,
>
> You should use Train*Vector*Classifier instead of TrainImagesClassifier.
> This will train and produce a SVM model ("-io.out" output file). You will
> have to set (at least) :
>
> - Input Vector Data (io.vd)
> - Output model (io.out)
> - Field names for training features (feat)
> - Field containing the class id for supervision (cfield, in your case
> it will be "training")
> - Classifier to use for the training (classifier = libsvm)
>
> However, before using this application, you will have to prepare a vector
> dataset with only your training polygons (polygons with a missing value in
> the field "training" will make the application crash).
>
> Once you have the output model file (simple text/xml file) you can use it
> to do the classification on the full dataset (using application
> OGRLayerClassification).
>
> Regards,
> Guillaume
>
> On 01/02/2017 11:54 PM, Geoffrey Balme wrote:
>
> I forgot to say that after the segmentation, I added a field in my vector
> attribute table named "training", and I gave some numeric values to few
> polygons based on the classes I want the segments to be classified in (3
> classes)
>
> So now I'm with a few polygons having a training value, and most of the
> polygon having no training value, and I want to do the svm classification
> from this.
>
> Le lundi 2 janvier 2017 23:52:47 UTC+1, Geoffrey Balme a écrit :
>>
>> Hi Guillaume,
>>
>> I'm also trying to do supervised object-based classification.
>> What I did so far is a meanshift segmentation on my satellite raster
>> image.
>> Then I did TrainImagesClassifier (svm) on the vector results of the
>> segmentation.
>> And at this point I'm not sure anymore what to do as there was no output
>> on the previous step.
>> Could you guide me from the segmentation to have an object-based
>> classification using SVM ?
>>
>> (I tried to do OGRLayerClassification, but had no idea of what to put in
>> the xml inputs etc..., the only input I have are my raster and my
>> segmentation vector result)
>>
>> Thank you !
>>
>> Le jeudi 24 novembre 2016 14:46:00 UTC+1, Guillaume Pasero a écrit :
>>>
>>> Hi Patricia,
>>>
>>> If I understand right, you want to perform object-based classification.
>>>
>>> In OTB-5.6.1 there is the application TrainVectorClassifier to train a
>>> classifier based on input geometries. You have to set several fields in the
>>> geometries you want to use for training :
>>>
>>> - one field to indicate the class of the geometry
>>>
>>> - several numeric fields to store the features attached to geometries.
>>>
>>> The fields can be extracted from a raster with LSMSVectorization and
>>> modified with QGis.
>>>
>>> After the training, you can either apply the model on the full set of
>>> geometries (with OGRLayerClassifier), or try to apply it on a raster (with
>>> ImageClassifier, but more difficult).
>>>
>>> Regards,
>>>
>>> Guillaume
>>> On 11/24/2016 02:09 PM, Patricia Lourenco wrote:
>>>
>>> Dear all,
>>> I am new with OTB-Monteverdi 5-6-1.
>>>
>>> I want to classify an image based on the segments created in the
>>> LSMVSVectorization (step 4 of the segmentation)using OTB/Monteverdi
>>> versions 5.6.1.
>>> However, I am not being able to do it.
>>>
>>> My questions are:
>>>
>>> 1. Which are the steps that I should take to do a classification based
>>> on segmentation?
>>>
>>> 2. Which OTB-Application should I use to select the segments for my
>>> classes?
>>>
>>> Thank you, in advance, for your help.
>>>
>>> Sincerely,
>>> Patricia
>>> --
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>>> --
>>> <http://www.c-s.fr> *Guillaume PASERO*
>>> Responsable technique
>>> *Business Unit E-SPACE & Geo Information - Département Image &
>>> Applications*
>>>
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> Responsable technique
> *Business Unit ESPACE & GeoInformation - Département Payload Data &
> Applications*
>
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> Parc de la Grande Plaine - 5, Rue Brindejonc des Moulinais - BP 15872
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>
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