Revision as of 13:07, 4 July 2019

Per pixel classification with QGIS and OTB processing plugin

In the search engine of Processing Toolbox, type TrainImages and open TrainImagesClassifer.
In the Input Image List select a (or optional: several) multispectral images: Subset_S2A_MSIL2A_20170619T_MUL.tif .
In the Validation Vector Data List and choose a vector polygon file with an independent sample of validation areas: lucc_validation.shp.
Type C_ID in the {button|text=Field Name}} text field.
Choose Support Vector Machine Classifer libsvm from the drop down list.
SVM Model Type is csvc
The SVM Kernel Type is Linear.
Switch the checkboxParameters optimization on.
In the Output model specify an model file: e.g. lucc_svm.model
Define an output file for Output confusion matrix or contingency table (e.g.lucc_svm_confusion.csv).
Click Run.

Type into the search box of the Windows taskbar: mapla.bat. Click on mapla.bat to open Monteverdi Application Launcher.
In the search engine of mapla, type TrainImages and double click TrainImagesClassifer.
In the Input Image List click on + and select a (or optional: several) multispectral images: Subset_S2A_MSIL2A_20170619T_MUL.tif .
In the Input Vector Data List choose a vector polygon file with training areas: lab07_training_input.shp.
Activate the checkbox Validation Vector Data List and choose a vector polygon file with an independent sample of validation areas: lab07_validation_input.shp
In the Output model specify an output model file: e.g. SVM.model
Activate the checkbox and save the Output confusion matrix or contingency table as ConfusionMatrixSVM.csv.
In the Bound sample number by minimum field type 1.
Set the training and validation sample ratio to 0. (0 = all training data).
Set Field containing the class integer label to C_ID (C_ID refers to the column that contains the LUC code in the training and validation vector file).
Choose LibSVM classifier from the drop down list as Classifier to use for the training.
The SVM Kernel Type is Linear.
Switch the Parameters optimization to on.
Set user defined seed with an integer value.
Click on Execute.

In the search engine of mapla, type ImageClassifier and double click ImageClassifier
Set Subset_S2A_MSIL2A_20170619T_MUL.tif as Input image.
Set SVM.model as Model file.
Save the Output image as svm_classification.tif.

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 * In the search engine of Processing Toolbox, type {{typed|text=TrainImages}} and open '''TrainImagesClassifer'''.
 * In the {{button|text=Input Image List}} select a (or optional: several) multispectral images: '''Subset_S2A_MSIL2A_20170619T_MUL.tif '''.
+* In the {{button|text=Validation Vector Data List}} and choose a vector polygon file with an independent sample of validation areas: '''lucc_validation.shp'''.
+* Type {{typed|text=C_ID}} in the {button|text=Field Name}} text field.
+* Choose Support Vector Machine Classifer {{button|text=libsvm}} from the drop down list.
+* SVM Model Type is {{button|text=csvc}}
+* The SVM Kernel Type is {{button|text=Linear}}.
+* Switch the checkbox{{button|text=Parameters optimization}} on.
+* In the {{button|text=Output model}} specify an model file: e.g. '''lucc_svm.model'''
+* Define an output file for {{button|text=Output confusion matrix or contingency table}} (e.g.'''lucc_svm_confusion.csv''').
+* Click {{typed|text=Run}}.
 ==Classification phase==