GEE实现监督分类并进行精度评估（基于哨兵影像）

具体的思路都写在注释里了

使用CART分类器对地物进行监督分类（基于Landsat 8）

// 建立一个矢量点
var pt = ee.Geometry.Point([118.55, 25.55]); 
// 筛选指定位置和指定时间的影像，并根据云量进行排序，选择云量最少的一幅影像
var landsat = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2').filterBounds(pt).filterDate('2023-01-01', '2024-01-01').sort('CLOUD_COVER').first(); 
// 将影像加载到地图中间
Map.centerObject(landsat, 8); 
// 将遥感影像加载到地图上 
var visParams = { bands: ['SR_B4', 'SR_B3', 'SR_B2'], min: 7000, max: 12000 
}; 
Map.addLayer(landsat, visParams, 'Landsat 8 image');
// 将特征合并为一个特征集合 
// .flatten()的主要作用是合并多个子集合中的特征，使得最终的特征集合包含所有的特征，而不是保持嵌套结构
var trainingFeatures = ee.FeatureCollection([tree, river, city]).flatten(); 
// 定义监督分类中所需的波段 
var predictionBands = ['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7','ST_B10' ]; 
// 提取每个样本训练点的特征信息
var classifierTraining = landsat.select(predictionBands).sampleRegions({ collection: trainingFeatures, properties: ['class'], scale: 30 
}); 
// 训练CART分类器
var classifier = ee.Classifier.smileCart().train({ features: classifierTraining, classProperty: 'class', inputProperties: predictionBands 
});
// 对影像进行分类
var classified = landsat.select(predictionBands).classify(classifier); 
// 定义影像的可视化参数
var classificationVis = { min: 0, max: 2, palette: ['589400', 'ff0000', '1a11ff'] // 自定义的色带
}; 
// 将分类结果添加到地图上
Map.addLayer(classified, classificationVis, 'CART classified'); 

使用RF分类器对地物进行监督分类（基于Landsat 8）

// 训练一个RF分类器
// RF分类需要定义构建的树的数量，代码中将树的数量定义为50
var RFclassifier = ee.Classifier.smileRandomForest(50).train({ features: classifierTraining, classProperty: 'class', inputProperties: predictionBands 
}); 
// 对影像进行分类
var RFclassified = landsat.select(predictionBands).classify(RFclassifier); 
// 定义影像的可视化参数 
var classificationVis = { min: 0, max: 2, palette: ['589400', 'ff0000', '1a11ff'] 
}; 
// 将分类结果添加到地图上
Map.addLayer(RFclassified, classificationVis, 'RFclassified'); 

使用随机森林进行地物分类及精度评价的完整代码（基于Sentinel-2）

// 选择研究区
var roi = ee.FeatureCollection('users/yongweicao11/wuyishan');var myImage = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED').filterBounds(roi).filterDate('2023-01-01', '2023-12-10').filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10));var composite = myImage.median().clip(roi);
// 以真彩色加载影像
var visParams = {bands: ['B4', 'B3', 'B2'],min: 0,max: 3000,
};
Map.centerObject(roi, 8);
Map.addLayer(composite, visParams, 'Cloud-free Composite');// 加入一些波段组合有助于提高分类精度
var mndwi = composite.normalizedDifference(['B3', 'B11']).rename('MNDWI');//计算MNDWI
var ndbi = composite.normalizedDifference(['B11', 'B8']).rename('NDBI');//计算NDBI
var ndvi = composite.normalizedDifference(['B8', 'B4']).rename('NDVI');//计算NDVI
composite = composite.addBands(ndvi).addBands(ndbi).addBands(mndwi)var trainingFeatures = ee.FeatureCollection([forest, water, city, soil]).flatten(); 
// 定义监督分类中所需的波段 
var predictionBands = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8','B9','B11','B12','MNDWI', 'NDBI', 'NDVI' ]; 
// 提取每个样本训练点的特征信息
var classifierTraining = composite.select(predictionBands).sampleRegions({ collection: trainingFeatures, properties: ['class'], scale: 30 
}); 
// RF分类需要定义构建的树的数量，代码中将树的数量定义为50
var RFclassifier = ee.Classifier.smileRandomForest(50).train({ features: classifierTraining, classProperty: 'class', inputProperties: predictionBands 
}); 
// 对影像进行分类
var RFclassified = composite.select(predictionBands).classify(RFclassifier); 
// 定义影像的可视化参数 
var classificationVis = { min: 0, max: 3, palette: ['589400', 'ff0000', '1a11ff', 'd0741e'] 
}; 
// 将分类结果添加到地图上
Map.addLayer(RFclassified, classificationVis, 'RFclassified')var data = classifierTraining
var trainingTesting = data.randomColumn(); 
var trainingSet = trainingTesting.filter(ee.Filter.lessThan('random', 0.8)); 
var testingSet = trainingTesting.filter(ee.Filter.greaterThanOrEquals('random', 0.8));var RFclassifier = ee.Classifier.smileRandomForest(50).train({ features: trainingSet, classProperty: 'class', inputProperties: predictionBands 
}); var confusionMatrix = testingSet.classify(RFclassifier).errorMatrix({actual: 'class', predicted: 'classification' 
});// Print the results. 
print('Confusion matrix:', confusionMatrix); 
print('Overall Accuracy:', confusionMatrix.accuracy()); 
print('Producers Accuracy:', 
confusionMatrix.producersAccuracy()); 
print('Consumers Accuracy:', 
confusionMatrix.consumersAccuracy()); 
print('Kappa:', confusionMatrix.kappa()); 

查看分类结果，可以在右上角控制台（console）查看混淆矩阵、整体精度和kappa系数等

参考书籍：《Cloud-Based Remote Sensing with Google Earth Engine》
（想要电子版可私信）

代码用到的矢量点数据（将以下代码复制到GEE代码框后convert一下就可以直接加载出我画好的矢量点）

var forest = /* color: #bf04c2 *//* shown: false */ee.FeatureCollection([ee.Feature(ee.Geometry.Point([118.04055013511174, 27.70693084385063]),{"class": 0,"system:index": "0"}),ee.Feature(ee.Geometry.Point([118.0421809181928, 27.71870852124089]),{"class": 0,"system:index": "1"}),ee.Feature(ee.Geometry.Point([117.9693106636762, 27.71316177354836]),{"class": 0,"system:index": "2"}),ee.Feature(ee.Geometry.Point([117.95789518210881, 27.703435288443746]),{"class": 0,"system:index": "3"}),ee.Feature(ee.Geometry.Point([117.97454633567327, 27.695607877859086]),{"class": 0,"system:index": "4"}),ee.Feature(ee.Geometry.Point([117.95746602866643, 27.69553188082996]),{"class": 0,"system:index": "5"}),ee.Feature(ee.Geometry.Point([117.9623583779096, 27.693631937905085]),{"class": 0,"system:index": "6"}),ee.Feature(ee.Geometry.Point([118.04303922507756, 27.72926935663225]),{"class": 0,"system:index": "7"}),ee.Feature(ee.Geometry.Point([118.03840436789983, 27.72919338305285]),{"class": 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