结果展示：
函数：ee.Classifier.libsvm()
主要参数：
例子：
// Sentinel-2表面反射图像，选择的反射波段，在这个设计的例子中用作训练和预测的来源。var img = ee.Image('COPERNICUS/S2_SR/0109T185751_0109T185931_T10SEG').select('B.*');// ESA WorldCover土地覆盖图，用作分类器训练中的标签源。var lc = ee.Image('ESA/WorldCover/v100/');// 将土地覆被类别值重新映射到基于0的连续序列。var classValues = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100];var remapValues = ee.List.sequence(0, 10);var label = 'lc';lc = lc.remap(classValues, remapValues).rename(label).toByte();// 添加土地覆被作为反射图像的波段，并从感兴趣区域内的每个土地覆被类别中以10米的比例采样100个像素。var roi = ee.Geometry.Rectangle(-122.347, 37.743, -122.024, 37.838);var sample = img.addBands(lc).stratifiedSample({numPoints: 100,classBand: label,region: roi,scale: 10,geometries: true});// 向样本中添加一个随机值字段，并使用它将大约80%的特征划分为定型集，20%的特征划分为验证集。sample = sample.randomColumn();var trainingSample = sample.filter('random <= 0.8');var validationSample = sample.filter('random > 0.8');// 从训练样本中训练SVM分类器(C-SVM分类、投票决策过程、线性核)。var trainedClassifier = ee.Classifier.libsvm().train({features: trainingSample,classProperty: label,inputProperties: img.bandNames()});// 获取有关已训练分类器的信息。print('Results of trained classifier', trainedClassifier.explain());// 获取训练样本的混淆矩阵和总体准确性。var trainAccuracy = trainedClassifier.confusionMatrix();print('Training error matrix', trainAccuracy);print('Training overall accuracy', trainAccuracy.accuracy());// 获得验证样本的混淆矩阵和总体精度。validationSample = validationSample.classify(trainedClassifier);var validationAccuracy = validationSample.errorMatrix(label, 'classification');print('Validation error matrix', validationAccuracy);print('Validation accuracy', validationAccuracy.accuracy());// 对来自训练好的分类器的反射图像进行分类。var imgClassified = img.classify(trainedClassifier);// 将图层添加到地图。var classVis = {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);
翻译至：/earth-engine/apidocs/ee-classifier-libsvm
