GEE_Random_Forest_Classifier

//code block 1
// Import country boundaries feature collection.
var dataset = ee.FeatureCollection('users/ashrujal/poc_eda_aoi-polygon');

// Apply filter where country name equals Nigeria.
var Portugal = dataset.filter(ee.Filter.eq('country_co', 'PO'));

// Print the "nigeria" object and explore features and properties.
// There should only be one feature representing Nigeria.
print('Portugal feature collection:', Portugal);

// Convert the Nigeria boundary feature collection to a line for map display.
var smBorder =
    ee.Image().byte().paint({featureCollection: Portugal, color: 1, width: 3});

// Set map options and add the Nigeria boundary as a layer to the map.
Map.setOptions('SATELLITE');
Map.centerObject(Portugal, 6);
Map.addLayer(smBorder, null, 'SM border');

// Import the maize target region asset.
var aoi = ee.FeatureCollection(
    'users/ashrujal/poc_eda_aoi-polygon');

// Display the maize target area boundary to the map.
Map.addLayer(aoi, {color: 'white', strokeWidth: 5}, 'AOI', true, 0.6);
Map.centerObject(aoi, 12);

// Import ground truth data that are divided into training and validation sets.
var trainingPts = ee.FeatureCollection(
    'users/ashrujal/trainingPts');
var validationPts = ee.FeatureCollection(
    'users/ashrujal/validationPts');

// Display training and validation points to see distribution within the AOI.
Map.addLayer(trainingPts, {color: 'green'}, 'Training points');
Map.addLayer(validationPts, {color: 'yellow'}, 'Validation points');


// Import S2 TOA reflectance and corresponding cloud probability collections.
var s2 = ee.ImageCollection('COPERNICUS/S2');
var s2c = ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY');

// Define dates over which to create a composite.
var start = ee.Date('2017-06-15');
var end = ee.Date('2017-10-15');

// Define a collection filtering function.
function filterBoundsDate(imgCol, aoi, start, end) {
  return imgCol.filterBounds(aoi).filterDate(start, end);
}

// Filter the collection by AOI and date.
s2 = filterBoundsDate(s2, aoi, start, end);
s2c = filterBoundsDate(s2c, aoi, start, end);

// Define a function to join the two collections on their 'system:index'
// property. The 'propName' parameter is the name of the property that
// references the joined image.
function indexJoin(colA, colB, propName) {
  var joined = ee.ImageCollection(ee.Join.saveFirst(propName).apply({
    primary: colA,
    secondary: colB,
    condition: ee.Filter.equals(
        {leftField: 'system:index', rightField: 'system:index'})
  }));
  // Merge the bands of the joined image.
  return joined.map(function(image) {
    return image.addBands(ee.Image(image.get(propName)));
  });
}

// Define a function to create a cloud masking function.
function buildMaskFunction(cloudProb) {
  return function(img) {
    // Define clouds as pixels having greater than the given cloud probability.
    var cloud = img.select('probability').gt(ee.Image(cloudProb));

    // Apply the cloud mask to the image and return it.
    return img.updateMask(cloud.not());
  };
}

// Join the cloud probability collection to the TOA reflectance collection.
var withCloudProbability = indexJoin(s2, s2c, 'cloud_probability');

// Map the cloud masking function over the joined collection, select only the
// reflectance bands.
var maskClouds = buildMaskFunction(50);
var s2Masked = ee.ImageCollection(withCloudProbability.map(maskClouds))
                   .select(ee.List.sequence(0, 12));

// Calculate the median of overlapping pixels per band.
var median = s2Masked.median();

// Calculate the difference between each image and the median.
var difFromMedian = s2Masked.map(function(img) {
  var dif = ee.Image(img).subtract(median).pow(ee.Image.constant(2));
  return dif.reduce(ee.Reducer.sum()).addBands(img).copyProperties(img, [
    'system:time_start'
  ]);
});

// Generate a composite image by selecting the pixel that is closest to the
// median.
var bandNames = difFromMedian.first().bandNames();
var bandPositions = ee.List.sequence(1, bandNames.length().subtract(1));
var mosaic = difFromMedian.reduce(ee.Reducer.min(bandNames.length()))
                 .select(bandPositions, bandNames.slice(1))
                 .clipToCollection(aoi);

// Display the mosaic.
Map.addLayer(
    mosaic, {bands: ['B11', 'B8', 'B3'], min: 225, max: 4000}, 'S2 mosaic');
    
    
    // Specify and select bands that will be used in the classification.
var bands = [
  'B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B10', 'B11',
  'B12'
];
var imageCl = mosaic.select(bands);

// Overlay the training points on the imagery to get a training sample; include
// the crop classification property ('class') in the sample feature collection.
var training = imageCl
                   .sampleRegions({
                     collection: trainingPts,
                     properties: ['class_int'],
                     scale: 30,
                     tileScale: 8
                   })
                   .filter(ee.Filter.neq(
                       'B1', null)); // Remove null pixels.
                       


                       
// Train a CART classifier with default parameters.
var trainedCart = ee.Classifier.smileCart().train(
    {features: training, classProperty: 'class_int', inputProperties: bands});

// Train a random forest classifier with default parameters.
var trainedRf = ee.Classifier.smileRandomForest({numberOfTrees: 10}).train({
  features: training,
  classProperty: 'class_int',
  inputProperties: bands
});

// Classify the image with the same bands used for training.
var classifiedCart = imageCl.select(bands).classify(trainedCart);
var classifiedRf = imageCl.select(bands).classify(trainedRf);

// Define visualization parameters for classification display.
var classVis = {min: 0, max: 7, palette: ['80FF00', '484848', 'ff0000', 'ffa500', 'CCF24D', '0000ff', '4b0082', 'ee82ee']};

// Add the output of the training classification to the map.
Map.addLayer(classifiedCart.clipToCollection(aoi), classVis, 'Classes (CART)');
Map.addLayer(
    classifiedRf.clipToCollection(aoi), classVis, 'Classes (RF)');

// Calculate the training error matrix and accuracy for both classifiers by
// using the "confusionMatrix" function to generate metrics on the
// resubstitution accuracy.
var trainAccuracyCart = trainedCart.confusionMatrix();
var trainAccuracyRf = trainedRf.confusionMatrix();

// Print model accuracy results.
print('##### TRAINING ACCURACY #####');
print('CART: overall accuracy:', trainAccuracyCart.accuracy());
print('RF: overall accuracy:', trainAccuracyRf.accuracy());
print('CART: error matrix:', trainAccuracyCart);
print('RF: error matrix:', trainAccuracyRf);

var corridor = ee.FeatureCollection('users/ashrujal/corridor')
corridor = corridor.geometry();
Map.centerObject(corridor);
Map.addLayer(corridor, {color: 'red'}, 'corridor');