Supervised classification (Tutorial)

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(→‎Training of per-pixel classifiers)
(→‎Classification with Orfeo Toolbox)
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== Classification==
 
== Classification==
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# Open {{mitem|text=Orfeo Toolbox --> Image Classification}} (see figure '''C''').
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* In the search engine of mapla, type {{typed|text=ImageClassifier}} and double click '''ImageClassifier'''
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# Set ''Subset_S2A_MSIL2A_20170619T_MUL_BOA.tif'' as {{button|text=Input image}}.
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* Set ''Subset_S2A_MSIL2A_20170619T_MUL.tif'' as {{button|text=Input image}}.
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# Set ''SVM.model'' as {{button|text=Model file}}.
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* Set '''SVM.model''' as {{button|text=Model file}}.
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# Set ''Subset_S2A_MSIL2A_20170619T_MUL_BOA.xml'' as {{button|text=Statistical file}}.
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* Save the {{button|text=Output image}} as '''svm_classification.tif'''.
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# Save the {{button|text=Output image}} as ''su_svm.tif''.
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# Evaluate classification results.
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* Evaluate classification results:
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## Add the classification result ''su_svm.tif'' to QGIS.
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** Add the result ''svm_classification.tif'' to a QGIS project.
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## Right click ''su_svm.tif'' in the [[TOC]] and select {{mitem|text=Properties --> Style --> Style --> Load Style}}.
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** Download the style file '''classifcation.qml''' from Stud.IP.
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## Load ''lab05_MinDist.qml''.
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** Right click ''svm_classifcation.tif'' in the [[TOC]] and select {{mitem|text=Properties --> Style --> Style --> Load Style}}.
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** Select the style file '''classifcation.qml'''. {{button|text=OK}}
  
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== Compute a confusion matrix with independent reference data ==
 
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# Open {{mitem|text=Orfeo Toolbox --> ComputeConfusionMatrix (Vector)}}.
 
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# Set ''su_svm.tif'' as {{button|text=Input image}}.
 
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# Set ''lab05_validation.shp'' as {{button|text=Input reference vector data}}.
 
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# Set {{button|text=Field name}} to ''C_ID''.
 
  
  

Revision as of 19:29, 8 December 2018

Classification with Orfeo Toolbox

Training per-pixel classifiers

  • 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 0.
  • 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 SVN Kernel Type is Gaussian radial basis function.
  • Switch the Parameters optimization to on.
  • Set user defined seed with an integer value.
  • Click on Execute.

500px

Classification

  • 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.
  • Evaluate classification results:
    • Add the result svm_classification.tif to a QGIS project.
    • Download the style file classifcation.qml from Stud.IP.
    • Right click svm_classifcation.tif in the TOC and select Properties --> Style --> Style --> Load Style.
    • Select the style file classifcation.qml. OK
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