Tea-Non Tea Classification using ML GEE

https://code.earthengine.google.com/148fa79456bbfbb0b68b468e922c4b51?noload=true


function maskLandsatclouds(image) {
var qa = image.select('BQA');
  var cloudBitMask = ee.Number(2).pow(4).int();
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0);
  return image.updateMask(mask)
      .select("B.*")
      .copyProperties(image, ["system:time_start"]);
}

var L5 = ee.ImageCollection("LANDSAT/LT05/C02/T2_L2")
  .filterDate('2010-02-01', '2010-03-30') 
  .filter(ee.Filter.lt("CLOUD_COVER",10))
  .filterBounds(table) 
  .map(maskLandsatclouds)
  //.select(L5bands);
  var image = L5.median().clip(table); 
print(image);

//Add indicies for Better Accuracy
var addIndices = function(image) {
  var ndvi = image.normalizedDifference(['B4', 'B3']).rename(['ndvi']);
  var ndbi = image.normalizedDifference(['B5', 'B4']).rename(['ndbi']);
  var mndwi = image.normalizedDifference(['B3', 'B5']).rename(['mndwi']); 
  var bsi = image.expression(
      '(( X + Y ) - (A + B)) /(( X + Y ) + (A + B)) ', {
        'X': image.select('B5'), //swir1
        'Y': image.select('B3'),  //red
        'A': image.select('B4'), // nir
        'B': image.select('B1'), // blue
  }).rename('bsi');
  return image.addBands(ndvi).addBands(ndbi).addBands(mndwi).addBands(bsi)
}

var Image1 = addIndices(image);
print(Image1, 'Image1');

var label = 'class' 
var bands = ['B1', 'B2', 'B3', 'B4','B5','B6','B7', 'ndvi', 'ndbi', 'mndwi', 'bsi']; // These are bands with 10 meter spatial resolution. 

print('BANDS',bands);



var visParamsTrue = {bands: ['B4', 'B3', 'B2'], min: 0, max: 0.4, gamma:1.2};
Map.addLayer(image, visParamsTrue, "Landsat 1990");
Map.centerObject(table, 8);

var gcps = Tea_plantation.merge(others);
print(gcps, 'gcps');

// selects the bands 
var Image1 = Image1.select(bands)
print(Image1,'IMAGE1');

// Overlay the point on the image to get training data.
var training = Image1.sampleRegions({
  collection: gcps, 
  properties: ['class'], 
  scale:  80
});

print(training, 'training');

// Add a random column and split the GCPs into training and validation set
var gcp = training.randomColumn()
print(gcp, 'gcp');
// This being a simpler classification, we take 60% points
// for validation. Normal recommended ratio is
// 70% training, 30% validation
var trainingGcp = gcp.filter(ee.Filter.lt('random', 0.6));
var validationGcp = gcp.filter(ee.Filter.gte('random', 0.6));

// Train a classifier.
var classifier = ee.Classifier.smileRandomForest(1000).train({
  features: training,  
  classProperty: 'class', 
  inputProperties: bands
});
print(classifier, 'classifier');

// // Classify the image.
var classified = Image1.classify(classifier);

Map.addLayer(classified, {min: 0, max: 1, palette: ['brown', 'green']}); 

//--------------------------Focal_Mode-----Majority---Filter----------------------------------

var image4 = classified.focal_mode();
Map.addLayer(image4, {min: 0, max: 1, palette: ['brown', 'green']})

//************************************************************************** 
// Accuracy Assessment
//************************************************************************** 

// Use classification map to assess accuracy using the validation fraction
// of the overall training set created above.
// Accuracy Assessment
//Classify the testingSet and get a confusion matrix.
 var confusionMatrix = ee.ConfusionMatrix(validationGcp.classify(classifier)
     .errorMatrix({
       actual: 'class', 
       predicted: 'classification'
     }));

print('Confusion matrix:', confusionMatrix);
print('Overall Accuracy:', confusionMatrix.accuracy());
print('Producers Accuracy:', confusionMatrix.producersAccuracy());
print('Consumers Accuracy:', confusionMatrix.consumersAccuracy());
print('Kappa statistic:', confusionMatrix.kappa());

//Select the class from the classified image
var tea = classified.select('classification').eq(0);//vegetation has 0 value in my case

//Calculate the pixel area in square kilometer
var area_tea = tea.multiply(ee.Image.pixelArea()).divide(1000000);

//Reducing the statistics for your study area
var stat = area_tea.reduceRegion ({
  reducer: ee.Reducer.sum(),
  geometry: table,
  scale: 60,
  maxPixels: 1e13
});

//Get the sq km area for Tea Plantation
print ('Tea Plantation Area (in sq.km)', stat);

//----------------------------Area Calculation--------------------------------------
//Select the class from the classified image
var tea1 = image4.select('classification').eq(0);//vegetation has 0 value in my case
//Calculate the pixel area in square kilometer
var area_tea1 = tea1.multiply(ee.Image.pixelArea()).divide(1000000);
//Reducing the statistics for your study area
var stat1 = area_tea1.reduceRegion ({
  reducer: ee.Reducer.sum(),
  geometry: table,
  scale: 80,
  maxPixels: 1e9
});
//Get the sq km area for Tea Plantation
print ('Tea Plantation Area (in sq.km)FOCAL', stat1);

Export.image.toDrive({
   image: image4,
   description: 'Tinsukia_2010',
   scale: 20,
   region: table,
   maxPixels: 1e13,
});