| 用量 | 傳回 |
|---|---|
ee.Classifier.libsvm(decisionProcedure, svmType, kernelType, shrinking, degree, gamma, coef0, cost, nu, terminationEpsilon, lossEpsilon, oneClass) | 分類器 |
| 引數 | 類型 | 詳細資料 |
|---|---|---|
decisionProcedure | 字串，預設值為「投票」 | 用於分類的決策程序。可以是「Voting」或「Margin」。不適用於迴歸。 |
svmType | 字串，預設值為「C_SVC」 | SVM 類型。`C_SVC`、`NU_SVC`、`ONE_CLASS`、`EPSILON_SVR` 或 `NU_SVR` 其中之一。 |
kernelType | 字串，預設值為「LINEAR」 | 核心類型。LINEAR (u′×v)、POLY ((γ×u′×v + coef₀)ᵈᵉᵍʳᵉᵉ)、RBF (exp(-γ×|u-v|²)) 或 SIGMOID (tanh(γ×u′×v + coef₀))。 |
shrinking | 布林值，預設值為 true | 是否使用縮減啟發式方法。 |
degree | 整數，預設值為 null | 多項式的次數。適用於 POLY 核心。 |
gamma | 浮點值，預設值為空值 | 核心函式中的 Gamma 值。預設值為特徵數量的倒數。適用於 POLY、RBF 和 SIGMOID 核心。 |
coef0 | 浮點值，預設值為空值 | 核心函式中的 coef₀ 值。預設值為 0。適用於 POLY 和 SIGMOID 核心。 |
cost | 浮點值，預設值為空值 | 費用 (C) 參數。默認為1。僅適用於 C-SVC、epsilon-SVR 和 nu-SVR。 |
nu | 浮點值，預設值為空值 | nu 參數。預設值為 0.5。僅適用於 nu-SVC、單一類別 SVM 和 nu-SVR。 |
terminationEpsilon | 浮點值，預設值為空值 | 終止條件容差 (e)。預設值為 0.001。僅適用於 epsilon-SVR。 |
lossEpsilon | 浮點值，預設值為空值 | 損失函式 (p) 中的 epsilon。預設值為 0.1。僅適用於 epsilon-SVR。 |
oneClass | 整數，預設值為 null | 單一類別 SVM 中要訓練的訓練資料類別。預設值為 0。僅適用於單一類別 SVM。可能的值為 0 和 1。分類器輸出內容為二進位 (0/1)，且會與判定為屬於該類別的資料相符。 |
範例
程式碼編輯器 (JavaScript)
// A Sentinel-2 surface reflectance image, reflectance bands selected, // serves as the source for training and prediction in this contrived example. var img = ee.Image('COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG') .select('B.*'); // ESA WorldCover land cover map, used as label source in classifier training. var lc = ee.Image('ESA/WorldCover/v100/2020'); // Remap the land cover class values to a 0-based sequential series. 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(); // 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. 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 }); // 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(); var trainingSample = sample.filter('random <= 0.8'); var validationSample = sample.filter('random > 0.8'); // Train an SVM classifier (C-SVM classification, voting decision procedure, // linear kernel) from the training sample. var trainedClassifier = 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. var trainAccuracy = 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); var validationAccuracy = validationSample.errorMatrix(label, 'classification'); print('Validation error matrix', validationAccuracy); print('Validation accuracy', validationAccuracy.accuracy()); // Classify the reflectance image from the trained classifier. var imgClassified = img.classify(trainedClassifier); // Add the layers to the map. 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);
import ee import geemap.core as geemap
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