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ee.Classifier.libsvm

  • Theee.Classifier.libsvm() function creates an empty Support Vector Machine classifier.

  • It returns a Classifier object and accepts various arguments to configure the SVM type, kernel, and other parameters.

  • The examples demonstrate how to use the classifier to train and classify an image using land cover data.

 Creates an empty Support Vector Machine classifier.

UsageReturns
ee.Classifier.libsvm(decisionProcedure,svmType,kernelType,shrinking,degree,gamma,coef0,cost,nu,terminationEpsilon,lossEpsilon,oneClass)Classifier
ArgumentTypeDetails
decisionProcedureString, default: "Voting"The decision procedure to use for classification. Either 'Voting' or 'Margin'. Not used for regression.
svmTypeString, default: "C_SVC"The SVM type. One of `C_SVC`, `NU_SVC`, `ONE_CLASS`, `EPSILON_SVR`, or `NU_SVR`.
kernelTypeString, default: "LINEAR"The kernel type. One of LINEAR (u′×v), POLY ((γ×u′×v + coef₀)ᵈᵉᵍʳᵉᵉ), RBF (exp(-γ×|u-v|²)), or SIGMOID (tanh(γ×u′×v + coef₀)).
shrinkingBoolean, default: trueWhether to use shrinking heuristics.
degreeInteger, default: nullThe degree of polynomial. Valid for POLY kernels.
gammaFloat, default: nullThe gamma value in the kernel function. Defaults to the reciprocal of the number of features. Valid for POLY, RBF, and SIGMOID kernels.
coef0Float, default: nullThe coef₀ value in the kernel function. Defaults to 0. Valid for POLY and SIGMOID kernels.
costFloat, default: nullThe cost (C) parameter. Defaults to 1. Only valid for C-SVC, epsilon-SVR, and nu-SVR.
nuFloat, default: nullThe nu parameter. Defaults to 0.5. Only valid for nu-SVC, one-class SVM, and nu-SVR.
terminationEpsilonFloat, default: nullThe termination criterion tolerance (e). Defaults to 0.001. Only valid for epsilon-SVR.
lossEpsilonFloat, default: nullThe epsilon in the loss function (p). Defaults to 0.1. Only valid for epsilon-SVR.
oneClassInteger, default: nullThe class of the training data on which to train in a one-class SVM. Defaults to 0. Only valid for one-class SVM. Possible values are 0 and 1. The classifier output is binary (0/1) and will match this class value for the data determined to be in the class.

Examples

Code Editor (JavaScript)

// A Sentinel-2 surface reflectance image, reflectance bands selected,// serves as the source for training and prediction in this contrived example.varimg=ee.Image('COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG').select('B.*');// ESA WorldCover land cover map, used as label source in classifier training.varlc=ee.Image('ESA/WorldCover/v100/2020');// Remap the land cover class values to a 0-based sequential series.varclassValues=[10,20,30,40,50,60,70,80,90,95,100];varremapValues=ee.List.sequence(0,10);varlabel='lc';lc=lc.remap(classValues,remapValues).rename(label).toByte();// Add land cover as a band of the reflectance image and sample 100 pixels at// 10 m scale from each land cover class within a region of interest.varroi=ee.Geometry.Rectangle(-122.347,37.743,-122.024,37.838);varsample=img.addBands(lc).stratifiedSample({numPoints:100,classBand:label,region:roi,scale:10,geometries:true});// Add a random value field to the sample and use it to approximately split 80%// of the features into a training set and 20% into a validation set.sample=sample.randomColumn();vartrainingSample=sample.filter('random <= 0.8');varvalidationSample=sample.filter('random > 0.8');// Train an SVM classifier (C-SVM classification, voting decision procedure,// linear kernel) from the training sample.vartrainedClassifier=ee.Classifier.libsvm().train({features:trainingSample,classProperty:label,inputProperties:img.bandNames()});// Get information about the trained classifier.print('Results of trained classifier',trainedClassifier.explain());// Get a confusion matrix and overall accuracy for the training sample.vartrainAccuracy=trainedClassifier.confusionMatrix();print('Training error matrix',trainAccuracy);print('Training overall accuracy',trainAccuracy.accuracy());// Get a confusion matrix and overall accuracy for the validation sample.validationSample=validationSample.classify(trainedClassifier);varvalidationAccuracy=validationSample.errorMatrix(label,'classification');print('Validation error matrix',validationAccuracy);print('Validation accuracy',validationAccuracy.accuracy());// Classify the reflectance image from the trained classifier.varimgClassified=img.classify(trainedClassifier);// Add the layers to the map.varclassVis={min:0,max:10,palette:['006400','ffbb22','ffff4c','f096ff','fa0000','b4b4b4','f0f0f0','0064c8','0096a0','00cf75','fae6a0']};Map.setCenter(-122.184,37.796,12);Map.addLayer(img,{bands:['B11','B8','B3'],min:100,max:3500},'img');Map.addLayer(lc,classVis,'lc');Map.addLayer(imgClassified,classVis,'Classified');Map.addLayer(roi,{color:'white'},'ROI',false,0.5);Map.addLayer(trainingSample,{color:'black'},'Training sample',false);Map.addLayer(validationSample,{color:'white'},'Validation sample',false);

Python setup

See the Python Environment page for information on the Python API and usinggeemap for interactive development.

importeeimportgeemap.coreasgeemap

Colab (Python)

# A Sentinel-2 surface reflectance image, reflectance bands selected,# serves as the source for training and prediction in this contrived example.img=ee.Image('COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG').select('B.*')# ESA WorldCover land cover map, used as label source in classifier training.lc=ee.Image('ESA/WorldCover/v100/2020')# Remap the land cover class values to a 0-based sequential series.class_values=[10,20,30,40,50,60,70,80,90,95,100]remap_values=ee.List.sequence(0,10)label='lc'lc=lc.remap(class_values,remap_values).rename(label).toByte()# Add land cover as a band of the reflectance image and sample 100 pixels at# 10 m scale from each land cover class within a region of interest.roi=ee.Geometry.Rectangle(-122.347,37.743,-122.024,37.838)sample=img.addBands(lc).stratifiedSample(numPoints=100,classBand=label,region=roi,scale=10,geometries=True)# Add a random value field to the sample and use it to approximately split 80%# of the features into a training set and 20% into a validation set.sample=sample.randomColumn()training_sample=sample.filter('random <= 0.8')validation_sample=sample.filter('random > 0.8')# Train an SVM classifier (C-SVM classification, voting decision procedure,# linear kernel) from the training sample.trained_classifier=ee.Classifier.libsvm().train(features=training_sample,classProperty=label,inputProperties=img.bandNames(),)# Get information about the trained classifier.display('Results of trained classifier',trained_classifier.explain())# Get a confusion matrix and overall accuracy for the training sample.train_accuracy=trained_classifier.confusionMatrix()display('Training error matrix',train_accuracy)display('Training overall accuracy',train_accuracy.accuracy())# Get a confusion matrix and overall accuracy for the validation sample.validation_sample=validation_sample.classify(trained_classifier)validation_accuracy=validation_sample.errorMatrix(label,'classification')display('Validation error matrix',validation_accuracy)display('Validation accuracy',validation_accuracy.accuracy())# Classify the reflectance image from the trained classifier.img_classified=img.classify(trained_classifier)# Add the layers to the map.class_vis={'min':0,'max':10,'palette':['006400','ffbb22','ffff4c','f096ff','fa0000','b4b4b4','f0f0f0','0064c8','0096a0','00cf75','fae6a0',],}m=geemap.Map()m.set_center(-122.184,37.796,12)m.add_layer(img,{'bands':['B11','B8','B3'],'min':100,'max':3500},'img')m.add_layer(lc,class_vis,'lc')m.add_layer(img_classified,class_vis,'Classified')m.add_layer(roi,{'color':'white'},'ROI',False,0.5)m.add_layer(training_sample,{'color':'black'},'Training sample',False)m.add_layer(validation_sample,{'color':'white'},'Validation sample',False)m

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Last updated 2024-07-13 UTC.