你有时候会不会有道这样的问题：
ConfusionMatrix (Error)
Property 'landcover' of feature '1_1_1_1_1_1_1_1_0_0' is missing.
我这里举一个例子，很多时候我们在进行混淆矩阵分析的时候都会出现这个状况，这个状况出现的主要原因是我们缺少了关键的属性名称。比如我们要做分类我们缺少了一个属性，需要”landcover“，在这之前我们首先要确保我们每一个所选的样本点都包含这个属性，但是不需要固定的名称，可以随便，只要在分类的时候让其拥有这个属性即可。
在此之前还需要确保每一个你选的样本点集合都是矢量集合，这样才能才可能继续分类。
解决方案：样本点属性设置进行修改
错误似乎出现在第11行，var newfc = urban.merge(vegetation).merge(water).merge(urban).merge(fields)；
确保urban和其他是配置几何导入工具中的一个FeatureCollection。
添加属性 "土地覆盖"，数值1代表城市，2代表水，以此类推。
你可以检查print(newfc)并查看属性，检查特征 "1_1_1_0_0"。
之前的代码：这里我们需要修改的不是代码本身而是样本点的属性，记住这一点就行了
var landsatCollection = ee.ImageCollection('LANDSAT/LC08/C01/T1') .filterDate('2017-01-01', '2017-12-31'); // Make a cloud-free composite. var composite = ee.Algorithms.Landsat.simpleComposite({ collection: landsatCollection, asFloat: true }); // Merge the three geometry layers into a single FeatureCollection. var newfc = urban.merge(vegetation).merge(water).merge(urban).merge(fields); // Use these bands for classification. var bands = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7']; // The name of the property on the points storing the class label. var classProperty = 'landcover'; // Sample the composite to generate training data. Note that the // class label is stored in the 'landcover' property. var training = composite.select(bands).sampleRegions({ collection: newfc, properties: [classProperty], scale: 30 }); // Train a CART classifier. var classifier = ee.Classifier.smileCart().train({ features: training, classProperty: [classProperty], }); // Print some info about the classifier (specific to CART). print('CART, explained', classifier.explain()); // Classify the composite. var classified = composite.classify(classifier); Map.centerObject(newfc); Map.addLayer(classified, {min: 0, max: 3, palette: ['red', 'blue', 'green','yellow']}); // Optionally, do some accuracy assessment. Fist, add a column of // random uniforms to the training dataset. var withRandom = training.randomColumn('random'); // We want to reserve some of the data for testing, to avoid overfitting the model. var split = 0.7; // Roughly 70% training, 30% testing. var trainingPartition = withRandom.filter(ee.Filter.lt('random', split)); var testingPartition = withRandom.filter(ee.Filter.gte('random', split)); // Trained with 70% of our data. var trainedClassifier = ee.Classifier.smileCart().train({ features: trainingPartition, classProperty: classProperty, inputProperties: bands }); // Classify the test FeatureCollection. var test = testingPartition.classify(trainedClassifier); // Print the confusion matrix. var confusionMatrix = test.errorMatrix(classProperty, 'classification'); print('Confusion Matrix', confusionMatrix);
