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edited Mar 9, 2018 at 13:51

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var year99=ee.Image('LANDSAT/LE07/C01/T1_SR/LE07_191025_19990915')
.clip(table)
.updateMask(FinalMask);

// Input image

var input=year99;

// Select and visualize MODIS land cover, IGBP classification, for training.

var modis=ee.Image('MODIS/051/MCD12Q1/2013_01_01')
.select('Land_Cover_Type_1')
.clip(table)
.updateMask(FinalMask);


// Define a palette for the 18 distinct land cover classes.

var 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
];

// Specify the min and max labels and the color palette matching the labels.

Map.setCenter(15.293, 50.659, 8);

Map.addLayer(modis,
             {min: 0, max: 17, palette: igbpPalette},
             'IGBP classification');


// Visualize input image

var visParams = {bands: ['B3', 'B2', 'B1'], min: 16, max: 1412};

Map.addLayer(input,visParams, 'input');

// Print input image and Modis Landcover image 

print ('input',input);
print ('modis',modis);


// Sample the input imagery to get a FeatureCollection of training data.
var training = input.addBands(modis).sample({
  numPixels: 500,
  seed: 0,
  region: table
});

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

// 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: 500,
  seed: 1,
  region:table
  // 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());


// Display the input and the classification.
Map.centerObject(table, 8);
Map.addLayer(input, {bands: ['B3', 'B2', 'B1'], max: 0.4}, 'landsat');
Map.addLayer(classified, {palette: igbpPalette, min: 0, max: 17},     'classification');


print('classified',classified);

var year99=ee.Image('LANDSAT/LE07/C01/T1_SR/LE07_191025_19990915')
.clip(table)
.updateMask(FinalMask);

// Input image

var input=year99;

// Select and visualize MODIS land cover, IGBP classification, for training.

var modis=ee.Image('MODIS/051/MCD12Q1/2013_01_01')
.select('Land_Cover_Type_1')
.clip(table)
.updateMask(FinalMask);


// Define a palette for the 18 distinct land cover classes.

var 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
];

// Specify the min and max labels and the color palette matching the labels.

Map.setCenter(15.293, 50.659, 8);

Map.addLayer(modis,
             {min: 0, max: 17, palette: igbpPalette},
             'IGBP classification');


// Visualize input image

var visParams = {bands: ['B3', 'B2', 'B1'], min: 16, max: 1412};

Map.addLayer(input,visParams, 'input');

// Print input image and Modis Landcover image 

print ('input',input);
print ('modis',modis);


// Sample the input imagery to get a FeatureCollection of training data.
var training = input.addBands(modis).sample({
  numPixels: 500,
  seed: 0,
  region: table
});

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

// 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: 500,
  seed: 1,
  region:table
  // 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());


// Display the input and the classification.
Map.centerObject(table, 8);
Map.addLayer(input, {bands: ['B3', 'B2', 'B1'], max: 0.4}, 'landsat');
Map.addLayer(classified, {palette: igbpPalette, min: 0, max: 17},     'classification');


print('classified',classified);

var year99=ee.Image('LANDSAT/LE07/C01/T1_SR/LE07_191025_19990915')
.clip(table)
.updateMask(FinalMask);

// Input image

var input=year99;

// Select and visualize MODIS land cover, IGBP classification, for training.

var modis=ee.Image('MODIS/051/MCD12Q1/2013_01_01')
.select('Land_Cover_Type_1')
.clip(table)
.updateMask(FinalMask);


// Define a palette for the 18 distinct land cover classes.

var 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
];

// Specify the min and max labels and the color palette matching the labels.

Map.setCenter(15.293, 50.659, 8);

Map.addLayer(modis,
             {min: 0, max: 17, palette: igbpPalette},
             'IGBP classification');


// Visualize input image

var visParams = {bands: ['B3', 'B2', 'B1'], min: 16, max: 1412};

Map.addLayer(input,visParams, 'input');

// Print input image and Modis Landcover image 

print ('input',input);
print ('modis',modis);


// Sample the input imagery to get a FeatureCollection of training data.
var training = input.addBands(modis).sample({
  numPixels: 500,
  seed: 0,
  region: table
});

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

// 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: 500,
  seed: 1,
  region:table
  // 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());


// Display the input and the classification.
Map.centerObject(table, 8);
Map.addLayer(input, {bands: ['B3', 'B2', 'B1'], max: 0.4}, 'landsat');
Map.addLayer(classified, {palette: igbpPalette, min: 0, max: 17},     'classification');


print('classified',classified);

var year99=ee.Image('LANDSAT/LE07/C01/T1_SR/LE07_191025_19990915')
.clip(table)
.updateMask(FinalMask);

// Input image

var input=year99;

// Select and visualize MODIS land cover, IGBP classification, for training.

var modis=ee.Image('MODIS/051/MCD12Q1/2013_01_01')
.select('Land_Cover_Type_1')
.clip(table)
.updateMask(FinalMask);


// Define a palette for the 18 distinct land cover classes.

var 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
];

// Specify the min and max labels and the color palette matching the labels.

Map.setCenter(15.293, 50.659, 8);

Map.addLayer(modis,
             {min: 0, max: 17, palette: igbpPalette},
             'IGBP classification');


// Visualize input image

var visParams = {bands: ['B3', 'B2', 'B1'], min: 16, max: 1412};

Map.addLayer(input,visParams, 'input');

// Print input image and Modis Landcover image 

print ('input',input);
print ('modis',modis);


// Sample the input imagery to get a FeatureCollection of training data.
var training = input.addBands(modis).sample({
  numPixels: 500,
  seed: 0,
  region: table
});

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

// 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: 500,
  seed: 1,
  region:table
  // 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());


// Display the input and the classification.
Map.centerObject(table, 8);
Map.addLayer(input, {bands: ['B3', 'B2', 'B1'], max: 0.4}, 'landsat');
Map.addLayer(classified, {palette: igbpPalette, min: 0, max: 17},     'classification');


print('classified',classified);

Source Link

asked Mar 9, 2018 at 10:46

roubalma

Exporting classification error matrix Google Earth Engine

I did a supervised classification of land cover and I need to export the Validation error matrix to .csv file. When using 'Export.table.toDrive' an error message 'Invalid argument: 'collection' must be a FeatureCollection' appears. Is there any way how to do that?

var year99=ee.Image('LANDSAT/LE07/C01/T1_SR/LE07_191025_19990915')
.clip(table)
.updateMask(FinalMask);

// Input image

var input=year99;

// Select and visualize MODIS land cover, IGBP classification, for training.

var modis=ee.Image('MODIS/051/MCD12Q1/2013_01_01')
.select('Land_Cover_Type_1')
.clip(table)
.updateMask(FinalMask);


// Define a palette for the 18 distinct land cover classes.

var 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
];

// Specify the min and max labels and the color palette matching the labels.

Map.setCenter(15.293, 50.659, 8);

Map.addLayer(modis,
             {min: 0, max: 17, palette: igbpPalette},
             'IGBP classification');


// Visualize input image

var visParams = {bands: ['B3', 'B2', 'B1'], min: 16, max: 1412};

Map.addLayer(input,visParams, 'input');

// Print input image and Modis Landcover image 

print ('input',input);
print ('modis',modis);


// Sample the input imagery to get a FeatureCollection of training data.
var training = input.addBands(modis).sample({
  numPixels: 500,
  seed: 0,
  region: table
});

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

// 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: 500,
  seed: 1,
  region:table
  // 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());


// Display the input and the classification.
Map.centerObject(table, 8);
Map.addLayer(input, {bands: ['B3', 'B2', 'B1'], max: 0.4}, 'landsat');
Map.addLayer(classified, {palette: igbpPalette, min: 0, max: 17},     'classification');


print('classified',classified);