對圖像進(jìn)行土地利用分類颓屑，因此下面是監(jiān)督分類的流程以及代碼案例禾进。
1.首先分類最開始應(yīng)該建立樣本數(shù)據(jù)集，在這里我分了四類酷勺，然后就開始自己的采樣戴已，設(shè)立好分類后固该，對目標(biāo)進(jìn)行分類。

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然后對每個樣本進(jìn)行顏色選擇和屬性定義

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//選擇需要裁剪的矢量數(shù)據(jù) 
var aoi = ee.FeatureCollection("users/yangyao19960805/NewFolder");
//加載矢量邊框糖儡，以便于在邊界內(nèi)選取樣本點(diǎn)
var empty = ee.Image().toByte();
var outline = empty.paint({
 featureCollection:aoi, // 行政邊界命名為fc
 color:0, //顏色透明
 width:3 //邊界寬度
});
Map.addLayer(outline, {palette: "ff0000"}, "outline");
//Function to mask the clouds in Sentinel-2
function maskS2clouds(image) {
  var qa = image.select('QA60');

  // Bits 10 and 11 are clouds and cirrus, respectively.
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;

  // Both flags should be set to zero, indicating clear conditions.
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0)
      .and(qa.bitwiseAnd(cirrusBitMask).eq(0));

  return image.updateMask(mask).divide(10000);
}

//Build the Sentinel 2 collection, filtered by date, bounds and percentage of cloud cover 
var dataset = ee.ImageCollection('COPERNICUS/S2_SR')
                  .filterDate('2019-01-01','2020-12-31')
                  .filterBounds(aoi)
                  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',10))
                  .map(maskS2clouds);
print("Sentinel 2 Image Collection",dataset);
var dem = ee.Image("NASA/NASADEM_HGT/001")
// Construct Classfication Dataset
// RS Index Cacluate(NDVI\NDWI\EVI\BSI)
var add_RS_index = function(img){
  var ndvi = img.normalizedDifference(['B8', 'B4']).rename('NDVI').copyProperties(img,['system:time_start']);
  var ndwi = img.normalizedDifference(['B3', 'B8']).rename('NDWI').copyProperties(img,['system:time_start']);
  var evi = img.expression('2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))', 
  {
    'NIR': img.select('B8'),
    'RED': img.select('B4'),
    'BLUE': img.select('B2')
  }).rename('EVI').copyProperties(img,['system:time_start']);
  var bsi = img.expression('((RED + SWIR1) - (NIR + BLUE)) / ((RED + SWIR1) + (NIR + BLUE)) ', 
  {
    'RED': img.select('B4'), 
    'BLUE': img.select('B2'),
    'NIR': img.select('B8'),
    'SWIR1': img.select('B11'),

  }).rename('BSI').copyProperties(img,['system:time_start']);


  var ibi = img.expression('(2 * SWIR1 / (SWIR1 + NIR) - (NIR / (NIR + RED) + GREEN / (GREEN + SWIR1))) / (2 * SWIR1 / (SWIR1 + NIR) + (NIR / (NIR + RED) + GREEN / (GREEN + SWIR1)))', {
    'SWIR1': img.select('B11'),
    'NIR': img.select('B8'),
    'RED': img.select('B4'),
    'GREEN': img.select('B3')
  }).rename('IBI').copyProperties(img,['system:time_start']);
  return img.addBands([ndvi, ndwi, evi, bsi, ibi]);
};
var dataset = dataset.map(add_RS_index); 
var bands = ['B2','B3','B4','B5','B6','B7','B8','B8A','B11','NDVI','NDWI','BSI'];
var imgcol_median = dataset.select(bands).median();
var aoi_dem = dem.select('elevation').clip(aoi).rename('DEM');
var construct_img = imgcol_median.addBands(aoi_dem).clip(aoi);
//分類樣本
var train_points = cropland.merge(grassland).merge(city).merge(forest).merge(water);
var train_data= construct_img.sampleRegions({
  collection: train_points,
  properties: ['landcover'],
  scale: 10
});
//精度評價
var withRandom = train_data.randomColumn('random');//樣本點(diǎn)隨機(jī)的排列
var split = 0.7; 
var trainingPartition = withRandom.filter(ee.Filter.lt('random', split));//篩選70%的樣本作為訓(xùn)練樣本
var testingPartition = withRandom.filter(ee.Filter.gte('random', split));//篩選30%的樣本作為測試樣本
//分類方法選擇隨機(jī)森林
var rf = ee.Classifier.smileRandomForest({
  numberOfTrees: 20,  
  bagFraction: 0.8
}).train({
  features: train_data,
  classProperty: 'landcover',
  // inputProperties: inputbands
});
//對哨兵數(shù)據(jù)進(jìn)行隨機(jī)森林分類
var img_classfication = construct_img.classify(rf); 
//運(yùn)用測試樣本分類伐坏，確定要進(jìn)行函數(shù)運(yùn)算的數(shù)據(jù)集以及函數(shù)
var test = testingPartition.classify(rf);
//計(jì)算混淆矩陣
var confusionMatrix = test.errorMatrix('landcover', 'classification');
print('confusionMatrix',confusionMatrix);//面板上顯示混淆矩陣
print('overall accuracy', confusionMatrix.accuracy());//面板上顯示總體精度
print('kappa accuracy', confusionMatrix.kappa());//面板上顯示kappa值
Map.centerObject(aoi)
Map.addLayer(aoi);
Map.addLayer(img_classfication.clip(aoi), {min: 1, max: 4, palette: ['orange', 'blue', 'green','yellow']});
var class1=img_classfication.clip(aoi)
//導(dǎo)出分類圖
Export.image.toDrive({  
       image: class1,  
       description: 'rfclass',  
       fileNamePrefix: 'rf',  //文件命名
       folder: "class",  //保存的文件夾
       scale: 10,  //分辨率
       region: aoi,  //研究區(qū)
       maxPixels: 1e13,  //最大像元素，默認(rèn)就好
       crs: "EPSG:4326"  //設(shè)置投影
   });  

分類好啦握联！

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