| 用法 | 返回 |
|---|---|
Classifier.explain() | 字典 |
| 参数 | 类型 | 详细信息 |
|---|---|---|
此：classifier | 分类器 | 要描述的分类器。 |
示例
代码编辑器 (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 a 10-tree random forest classifier from the training sample. var trainedClassifier = ee.Classifier.smileRandomForest(10).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 a 10-tree random forest classifier from the training sample. trained_classifier = ee.Classifier.smileRandomForest(10).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