confusion_matrix.py

import ee 
import geemap

# Create a map centered at (lat, lon).
Map = geemap.Map(center=[40, -100], zoom=4)

# Define a region of interest as a point.  Change the coordinates
# to get a classification of any place where there is imagery.
roi = ee.Geometry.Point(-122.3942, 37.7295)

# Load Landsat 5 input imagery.
landsat = ee.Image(ee.ImageCollection('LANDSAT/LT05/C01/T1_TOA')
  # Filter to get only one year of images. \
  .filterDate('2011-01-01', '2011-12-31')
  # Filter to get only images under the region of interest. \
  .filterBounds(roi)
  # Sort by scene cloudiness, ascending. \
  .sort('CLOUD_COVER')
  # Get the first (least cloudy) scene. \
  .first())

# Compute cloud score.
cloudScore = ee.Algorithms.Landsat.simpleCloudScore(landsat).select('cloud')

# Mask the input for clouds.  Compute the min of the input mask to mask
# pixels where any band is masked.  Combine that with the cloud mask.
input = landsat.updateMask(landsat.mask().reduce('min').And(cloudScore.lte(50)))

# Use MODIS land cover, IGBP classification, for training.
modis = ee.Image('MODIS/051/MCD12Q1/2011_01_01') \
    .select('Land_Cover_Type_1')

# Sample the input imagery to get a FeatureCollection of training data.
training = input.addBands(modis).sample(**{
  'numPixels': 5000,
  'seed': 0
})

# Make a Random Forest classifier and train it.
classifier = ee.Classifier.randomForest(10) \
    .train(training, 'Land_Cover_Type_1')

# Classify the input imagery.
classified = input.classify(classifier)

# Get a confusion matrix representing resubstitution accuracy.
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.
validation = input.addBands(modis).sample(**{
  'numPixels': 5000,
  'seed': 1
  # Filter the result to get rid of any {} pixels.
}).filter(ee.Filter.neq('B1', {}))

# Classify the validation data.
validated = validation.classify(classifier)

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

# Define a palette for the IGBP classification.
igbpPalette = [
  'aec3d4', # water
  '152106', '225129', '369b47', '30eb5b', '387242', # forest
  '6a2325', 'c3aa69', 'b76031', 'd9903d', '91af40',  # shrub, grass
  '111149', # wetlands
  'cdb33b', # croplands
  'cc0013', # urban
  '33280d', # crop mosaic
  'd7cdcc', # snow and ice
  'f7e084', # barren
  '6f6f6f'  # tundra
]

# Display the input and the classification.
Map.centerObject(ee.FeatureCollection(roi), 10)
Map.addLayer(input, {'bands': ['B3', 'B2', 'B1'], 'max': 0.4}, 'landsat')
Map.addLayer(classified, {'palette': igbpPalette, 'min': 0, 'max': 17}, 'classification')


# Display the map.
Map