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ee.Image.classify
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
对图片进行分类。
用法 | 返回 |
---|
Image.classify(classifier, outputName) | 图片 |
参数 | 类型 | 详细信息 |
---|
此:image | 图片 | 要分类的图片。系统会按名称从此图片中提取波段,并且该图片必须包含分类器架构中命名的所有波段。 |
classifier | 分类器 | 要使用的分类器。 |
outputName | 字符串,默认值:“classification” | 要添加的频段的名称。如果分类器生成多个输出,则忽略此名称。 |
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);
Python 设置
如需了解 Python API 和如何使用 geemap
进行交互式开发,请参阅
Python 环境页面。
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
如未另行说明,那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可,并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情,请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC):2025-07-26。
[null,null,["最后更新时间 (UTC):2025-07-26。"],[[["\u003cp\u003eClassifies an image using a pre-trained classifier.\u003c/p\u003e\n"],["\u003cp\u003eTakes an image and a classifier as input.\u003c/p\u003e\n"],["\u003cp\u003eReturns a classified image with a new band named "classification" (by default) containing the classification results.\u003c/p\u003e\n"],["\u003cp\u003eCan be used for various image classification tasks, such as land cover mapping.\u003c/p\u003e\n"]]],["The core process involves classifying an image using a trained classifier. First, a classifier is trained on a sample dataset with labeled land cover data. This training includes splitting the data into training and validation sets to assess the classifier's performance using error matrices and accuracy metrics. Finally, the trained classifier is applied to a reflectance image, generating a classified output. This output can then be visualized alongside the original image and the training data.\n"],null,["# ee.Image.classify\n\nClassifies an image.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|----------------------------------------------|---------|\n| Image.classify`(classifier, `*outputName*`)` | Image |\n\n| Argument | Type | Details |\n|---------------|-----------------------------------|----------------------------------------------------------------------------------------------------------------------------------------|\n| this: `image` | Image | The image to classify. Bands are extracted from this image by name and it must contain all the bands named in the classifier's schema. |\n| `classifier` | Classifier | The classifier to use. |\n| `outputName` | String, default: \"classification\" | The name of the band to be added. If the classifier produces more than 1 output, this name is ignored. |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\n// A Sentinel-2 surface reflectance image, reflectance bands selected,\n// serves as the source for training and prediction in this contrived example.\nvar img = ee.Image('COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG')\n .select('B.*');\n\n// ESA WorldCover land cover map, used as label source in classifier training.\nvar lc = ee.Image('ESA/WorldCover/v100/2020');\n\n// Remap the land cover class values to a 0-based sequential series.\nvar classValues = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100];\nvar remapValues = ee.List.sequence(0, 10);\nvar label = 'lc';\nlc = lc.remap(classValues, remapValues).rename(label).toByte();\n\n// Add land cover as a band of the reflectance image and sample 100 pixels at\n// 10 m scale from each land cover class within a region of interest.\nvar roi = ee.Geometry.Rectangle(-122.347, 37.743, -122.024, 37.838);\nvar sample = img.addBands(lc).stratifiedSample({\n numPoints: 100,\n classBand: label,\n region: roi,\n scale: 10,\n geometries: true\n});\n\n// Add a random value field to the sample and use it to approximately split 80%\n// of the features into a training set and 20% into a validation set.\nsample = sample.randomColumn();\nvar trainingSample = sample.filter('random \u003c= 0.8');\nvar validationSample = sample.filter('random \u003e 0.8');\n\n// Train a 10-tree random forest classifier from the training sample.\nvar trainedClassifier = ee.Classifier.smileRandomForest(10).train({\n features: trainingSample,\n classProperty: label,\n inputProperties: img.bandNames()\n});\n\n// Get information about the trained classifier.\nprint('Results of trained classifier', trainedClassifier.explain());\n\n// Get a confusion matrix and overall accuracy for the training sample.\nvar trainAccuracy = trainedClassifier.confusionMatrix();\nprint('Training error matrix', trainAccuracy);\nprint('Training overall accuracy', trainAccuracy.accuracy());\n\n// Get a confusion matrix and overall accuracy for the validation sample.\nvalidationSample = validationSample.classify(trainedClassifier);\nvar validationAccuracy = validationSample.errorMatrix(label, 'classification');\nprint('Validation error matrix', validationAccuracy);\nprint('Validation accuracy', validationAccuracy.accuracy());\n\n// Classify the reflectance image from the trained classifier.\nvar imgClassified = img.classify(trainedClassifier);\n\n// Add the layers to the map.\nvar classVis = {\n min: 0,\n max: 10,\n palette: ['006400' ,'ffbb22', 'ffff4c', 'f096ff', 'fa0000', 'b4b4b4',\n 'f0f0f0', '0064c8', '0096a0', '00cf75', 'fae6a0']\n};\nMap.setCenter(-122.184, 37.796, 12);\nMap.addLayer(img, {bands: ['B11', 'B8', 'B3'], min: 100, max: 3500}, 'img');\nMap.addLayer(lc, classVis, 'lc');\nMap.addLayer(imgClassified, classVis, 'Classified');\nMap.addLayer(roi, {color: 'white'}, 'ROI', false, 0.5);\nMap.addLayer(trainingSample, {color: 'black'}, 'Training sample', false);\nMap.addLayer(validationSample, {color: 'white'}, 'Validation sample', false);\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\n# A Sentinel-2 surface reflectance image, reflectance bands selected,\n# serves as the source for training and prediction in this contrived example.\nimg = ee.Image(\n 'COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG'\n).select('B.*')\n\n# ESA WorldCover land cover map, used as label source in classifier training.\nlc = ee.Image('ESA/WorldCover/v100/2020')\n\n# Remap the land cover class values to a 0-based sequential series.\nclass_values = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100]\nremap_values = ee.List.sequence(0, 10)\nlabel = 'lc'\nlc = lc.remap(class_values, remap_values).rename(label).toByte()\n\n# Add land cover as a band of the reflectance image and sample 100 pixels at\n# 10 m scale from each land cover class within a region of interest.\nroi = ee.Geometry.Rectangle(-122.347, 37.743, -122.024, 37.838)\nsample = img.addBands(lc).stratifiedSample(\n numPoints=100, classBand=label, region=roi, scale=10, geometries=True\n)\n\n# Add a random value field to the sample and use it to approximately split 80%\n# of the features into a training set and 20% into a validation set.\nsample = sample.randomColumn()\ntraining_sample = sample.filter('random \u003c= 0.8')\nvalidation_sample = sample.filter('random \u003e 0.8')\n\n# Train a 10-tree random forest classifier from the training sample.\ntrained_classifier = ee.Classifier.smileRandomForest(10).train(\n features=training_sample,\n classProperty=label,\n inputProperties=img.bandNames(),\n)\n\n# Get information about the trained classifier.\ndisplay('Results of trained classifier', trained_classifier.explain())\n\n# Get a confusion matrix and overall accuracy for the training sample.\ntrain_accuracy = trained_classifier.confusionMatrix()\ndisplay('Training error matrix', train_accuracy)\ndisplay('Training overall accuracy', train_accuracy.accuracy())\n\n# Get a confusion matrix and overall accuracy for the validation sample.\nvalidation_sample = validation_sample.classify(trained_classifier)\nvalidation_accuracy = validation_sample.errorMatrix(label, 'classification')\ndisplay('Validation error matrix', validation_accuracy)\ndisplay('Validation accuracy', validation_accuracy.accuracy())\n\n# Classify the reflectance image from the trained classifier.\nimg_classified = img.classify(trained_classifier)\n\n# Add the layers to the map.\nclass_vis = {\n 'min': 0,\n 'max': 10,\n 'palette': [\n '006400',\n 'ffbb22',\n 'ffff4c',\n 'f096ff',\n 'fa0000',\n 'b4b4b4',\n 'f0f0f0',\n '0064c8',\n '0096a0',\n '00cf75',\n 'fae6a0',\n ],\n}\nm = geemap.Map()\nm.set_center(-122.184, 37.796, 12)\nm.add_layer(\n img, {'bands': ['B11', 'B8', 'B3'], 'min': 100, 'max': 3500}, 'img'\n)\nm.add_layer(lc, class_vis, 'lc')\nm.add_layer(img_classified, class_vis, 'Classified')\nm.add_layer(roi, {'color': 'white'}, 'ROI', False, 0.5)\nm.add_layer(training_sample, {'color': 'black'}, 'Training sample', False)\nm.add_layer(\n validation_sample, {'color': 'white'}, 'Validation sample', False\n)\nm\n```"]]