问image.sampleRegions不是一种功能( google有监督的分类)

EN

/**
 * Function to mask clouds using the Sentinel-2 QA band
 * @param {ee.Image} image Sentinel-2 image
 * @return {ee.Image} cloud masked Sentinel-2 image
 */
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);
}

var dataset = ee.ImageCollection('COPERNICUS/S2_SR')
                  .filterDate('2019-09-01', '2019-10-01') //september
                  // Pre-filter to get less cloudy granules.
                  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',20))
                  .map(maskS2clouds);

var visualization = {
  min: 0.0,
  max: 0.3,
  bands: ['B4', 'B3', 'B2'],
};

Map.setCenter(101.68287285738528,0.6988384299139916, 16);
Map.addLayer(dataset.mean(), visualization, 'RGB');

// Use these bands for prediction.
var bands = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B10', 'B11'];


// Make a FeatureCollection from the hand-made geometries.
var polygons = ee.FeatureCollection([
  ee.Feature(Kebun1, {'class': 0}),
  ee.Feature(Kebun2, {'class': 0}),
  ee.Feature(Kebun3, {'class': 0}),
  ee.Feature(Canal1, {'class': 1}),
  ee.Feature(Canal2, {'class': 1}),
]);

//Define the image aduh anjir salah dimana sih
var imageCollection = ee.ImageCollection("COPERNICUS/S2");
var geometry = ee.FeatureCollection(polygons);
var image = imageCollection
.filterDate('2019-09-01', '2019-10-1')
                  // Pre-filter to get less cloudy granules.
                  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))
                  .map(maskS2clouds)
                  //filter according to drawn boundary
.filterBounds(geometry);

             

// Get the values for all pixels in each polygon in the training.
var training = image.sampleRegions({
  // Get the sample from the polygons FeatureCollection.
  collection: polygons,
  // Keep this list of properties from the polygons.
  properties: ['class'],
  // Set the scale to get Landsat pixels in the polygons.
  scale: 30
});

// Create an SVM classifier with custom parameters.
var classifier = ee.Classifier.libsvm({
  kernelType: 'RBF',
  gamma: 0.5,
  cost: 10
});

// Train the classifier.
var trained = classifier.train(training, 'class', bands);

// Classify the image.
var classified = image.classify(trained);

// Display the classification result and the input image.
Map.setCenter(101.68287285738528,0.6988384299139916,16);
Map.addLayer(image, {bands: ['B4', 'B3', 'B2'], max: 0.5, gamma: 2});
Map.addLayer(polygons, {}, 'training polygons');
Map.addLayer(classified,
             {min: 0, max: 1, palette: ['red', 'green']},
             'klasifikasi');