Accuracy assessment using Google Earth Engine?

Question

I'm using Google Earth Engine for Land Cover change detection, I would like to know whether we can generate Error Matrix (Accuracy Assessment) for a classified image in Earth Engine or not?

Answer 1

Yes, you can. This page describes how to do it.

// Make a Random Forest classifier and train it.
var classifier = ee.Classifier.randomForest(10)
    .train({
      features: training, 
      classProperty: 'Land_Cover_Type_1',
      inputProperties: ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7']
    });

// Classify the input imagery.
var classified = input.classify(classifier);

// Get a confusion matrix representing resubstitution accuracy.
var trainAccuracy = classifier.confusionMatrix();
print('Resubstitution error matrix: ', trainAccuracy);
print('Training overall accuracy: ', trainAccuracy.accuracy());

// Sample the input with a different random seed to get validation data.
var validation = input.addBands(modis).sample({
  numPixels: 5000,
  seed: 1
  // Filter the result to get rid of any null pixels.
}).filter(ee.Filter.neq('B1', null));

// Classify the validation data.
var validated = validation.classify(classifier);

// Get a confusion matrix representing expected accuracy.
var testAccuracy = validated.errorMatrix('Land_Cover_Type_1', 'classification');
print('Validation error matrix: ', testAccuracy);
print('Validation overall accuracy: ', testAccuracy.accuracy());

It's worth noting that you can also take a single sample and partition it:

// The randomColumn() method will add a column of uniform random
// numbers in a column named 'random' by default.
var sample = input.addBands(modis).sample({...}).randomColumn()
var split = 0.7;  // Roughly 70% training, 30% testing.
var training = sample.filter(ee.Filter.lt('random', split));
var testing = sample.filter(ee.Filter.gte('random', split));