问题：
I'm trying to use a classifier to classify the land use of Landsat images, but when I use the function which is "classifier. conflusionMatrix", I get an error as follows: Classifier confusionMatrix: Property 'type' of feature '00000000000000000014_ 0' is missing. (Error code: 3). I don't know how to correct it. I really need your help to solve this problem. By the way, I finally remembered to share the assets.
我正在尝试使用分类器对 Landsat 图像的土地利用进行分类，但是当我使用“classifier.conflusionMatrix”函数时，出现如下错误：Classifier confusionMatrix: Property 'type' of feature '000000000000000000014_ 0 ' 不见了。 （错误代码：3）。我不知道如何纠正它。我真的需要你的帮助来解决这个问题。对了，我终于记得分享资产了。
This is the link:
https://code.earthengine.google.com/d789c5e1a86b1406dbb93d5d9420d4a5?noload=1
代码：
var table = ee.FeatureCollection("users/yongxin/henan_bese"), table3 = ee.FeatureCollection("users/yongxin/zhengzhou_sample"); //确定区域 var table = ee.FeatureCollection("users/yongxin/henan_bese"); var roi = ee.Geometry.Point([113.663221,34.7568711]); var sCol = table.filterBounds(roi); Map.centerObject(sCol,6); Map.addLayer(sCol,{color:"red"},"zhengzhou"); /** forest 0 urban 1 paddyrice 2 water 3 crop 4 */ //iazai加载yangbendianshujv加载样本点数据 var sampleData = ee.FeatureCollection("users/yongxin/zhengzhou_sample"); Map.addLayer(sampleData, {}, "sampleData"); //Landsat8 SR数据去云 function rmCloud(image) { var cloudShadowBitMask = (1 << 3); var cloudsBitMask = (1 << 5); var qa = image.select("pixel_qa"); var mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0) .and(qa.bitwiseAnd(cloudsBitMask).eq(0)); return image.updateMask(mask); } //缩放 function scaleImage(image) { var time_start = image.get("system:time_start"); image = image.multiply(0.0001); image = image.set("system:time_start", time_start); return image; } //NDVI function NDVI(image) { return image.addBands( image.normalizedDifference(["B5", "B4"]) .rename("NDVI")); } //NDWI function NDWI(image) { return image.addBands( image.normalizedDifference(["B3", "B5"]) .rename("NDWI")); } //NDBI function NDBI(image) { return image.addBands( image.normalizedDifference(["B6", "B5"]) .rename("NDBI")); } //加载影像数据（未分类的Landsat影像数据） var l8Col = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR") .filterBounds(sCol) .filterDate("2016-1-1", "2017-1-1") .map(rmCloud) .map(scaleImage) .map(NDVI) .map(NDWI) .map(NDBI); //DEM var srtm = ee.Image("USGS/SRTMGL1_003"); var dem = ee.Algorithms.Terrain(srtm); var elevation = dem.select("elevation"); var slope = dem.select("slope"); //定义波段 var bands = [ "B1", "B2", "B3", "B4", "B5", "B6", "B7", "NDBI", "NDWI", "NDVI","SLOPE", "ELEVATION" ]; //影像处理 var l8Image = l8Col.median() .addBands(elevation.rename("ELEVATION")) .addBands(slope.rename("SLOPE")) .clip(sCol) .select(bands); //rgb参数设置 var rgbVisParam = { min: 0, max: 0.3, bands: ["B4", "B3", "B2"] }; Map.addLayer(l8Image, rgbVisParam, "l8Image"); //切分生成训练数据和验证数据 sampleData = sampleData.randomColumn('random'); var sample_training = sampleData.filter(ee.Filter.lte("random", 0.7)); var sample_validate = sampleData.filter(ee.Filter.gt("random", 0.7)); print("sample_training", sample_training); print("sample_validate", sample_validate); // //生成监督分类训练使用的样本数据 // var training = l8Image.sampleRegions({ // collection: sample_training, // properties: ["type"], // scale: 30 // }); // //生成监督分类验证使用的样本数据 // var validation = l8Image.sampleRegions({ // collection: sample_validate, // properties: ["type"], // scale: 30 // }); // //初始化分类器 // var classifier = ee.Classifier.smileCart().train({ // features: training, // classProperty: "type", // inputProperties: bands // }); //生成监督分类训练使用的样本数据 var training = l8Image.sampleRegions({ collection: sample_training, properties: ["class"], scale: 30 }); //生成监督分类验证使用的样本数据 var validation = l8Image.sampleRegions({ collection: sample_validate, properties: ["class"], scale: 30 }); //初始化分类器 var classifier = ee.Classifier.smileCart().train({ features: training, classProperty: "class", inputProperties: bands }); //影像数据调用classify利用训练数据训练得到分类结果 var classified = l8Image.classify(classifier); //训练结果的混淆矩阵 var trainAccuracy = classifier.confusionMatrix(); //导出训练精度结果CSV Export.table.toDrive({ collection: ee.FeatureCollection([ ee.Feature(null, { matrix: trainAccuracy.array(), kappa: trainAccuracy.kappa(), accuracy: trainAccuracy.accuracy() } )]), description: "l8TrainConf_zhengzhou", folder:"training_zhengzhou", fileFormat: "CSV" }); //导出影像 var resultImg = classified.clip(sCol).toByte(); resultImg = resultImg.remap([0,1,2,3,4], [1,2,3,4,5]); Export.image.toAsset({ image: resultImg, description: 'Asset-l8Classifiedmap_zhengzhou', assetId: "training_zhengzhou/l8Classifiedmap_zhengzhou", region: sCol, scale:30, crs: "EPSG:4326", maxPixels: 1e13 }); //影像导出 Export.image.toDrive({ image:resultImg, description:'Drive-l8Classifiedmap_zhengzhou', fileNamePrefix: "l8Classifiedmap_zhengzhou", folder:"training_zhengzhou", region: sCol, scale:30, crs: "EPSG:4326", maxPixels:1e13 });
我们根据提示可以看出，文件中不存在属性，所以我们需要查看我们打印或者输出的结果中到底有没有这个属性，可以看到这个属性中并没有“type”属性。我们要注意的是这里我们所需的不是矢量文件中每个属性，这是值得注意的，这里选择的就是分类后产生的新的分类属性。
矢量中的类型“type”代表的并不是属性列表，而是表明矢量类型就是矢量。 
需要修改的代码：
//生成监督分类训练使用的样本数据 var training = l8Image.sampleRegions({ collection: sample_training, properties: ["class"], scale: 30 }); //生成监督分类验证使用的样本数据 var validation = l8Image.sampleRegions({ collection: sample_validate, properties: ["class"], scale: 30 }); //初始化分类器 var classifier = ee.Classifier.smileCart().train({ features: training, classProperty: "class", inputProperties: bands });
修改后成功的下载结果
