Land cover classification using supervised learning method in Google Earth Engine¶

Overview

In this project, I try to classify the land use land cover using Random Forest Classifier in Google Earth Engine by:

using the images from Sentinel-2 satellite in January 2022

using Random Forest Classifier with parameter of 300 trees

using the training data from 1000 random points with the label from ESA WorldCover dataset.

Objective

Classify land use and land cover in Google Earth Engine and then visualize it in GEE App.

Code

The script to classify the land cover type is detailed as follows:

var roi = ee.Geometry.Polygon([[[103.63665096774587, 13.584793535905758],
        [103.63665096774587, 13.264209644744009],
        [104.07061092868337, 13.264209644744009],
        [104.07061092868337, 13.584793535905758]]], null, false);

var baseChange = [{featureType: 'all', stylers: [{invert_lightness: true}]}];

Map.setOptions('baseChange', {'baseChange': baseChange});
Map.centerObject(roi, 10)

/**
* Function to mask clouds using the Sentinel-2 QA band
* @param {ee.Image} image Sentinel-2 image
* @return {ee.Image} cloud masked Sentinel-2 image
*/
function maskS2clouds(image) {
var qa = image.select('QA60');

// Bits 10 and 11 are clouds and cirrus, respectively.
var cloudBitMask = 1 << 10;
var cirrusBitMask = 1 << 11;

// Both flags should be set to zero, indicating clear conditions.
var mask = qa.bitwiseAnd(cloudBitMask).eq(0)
    .and(qa.bitwiseAnd(cirrusBitMask).eq(0));

return image.updateMask(mask).divide(10000);
}

var dataset = ee.ImageCollection('COPERNICUS/S2_SR')
                .filterDate('2022-01-01', '2022-01-30')
                // Pre-filter to get less cloudy granules.
                .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',5))
                .map(maskS2clouds);

var visualization = {
min: 0.0,
max: 0.3,
bands: ['B4', 'B3', 'B2'],
};

var img = ee.Image(dataset.mean()).select('B.*');

// ESA WorldCover land cover map, used as label source in classifier training.
var lc = ee.Image('ESA/WorldCover/v100/2020');

// Remap the land cover class values to a 0-based sequential series.
var classValues = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100];
var remapValues = ee.List.sequence(0, 10);
var label = 'lc';
lc = lc.remap(classValues, remapValues).rename(label).toByte();

// Add land cover as a band of the reflectance image and sample 100 pixels at
// 10 m scale from each land cover class within a region of interest.

var sample = img.addBands(lc).stratifiedSample({
numPoints: 1000,
classBand: label,
region: roi,
scale: 10,
geometries: true
});

// Add a random value field to the sample and use it to approximately split 80%
// of the features into a training set and 20% into a validation set.
sample = sample.randomColumn();
var trainingSample = sample.filter('random <= 0.8');
var validationSample = sample.filter('random > 0.8');

// Train a 10-tree random forest classifier from the training sample.
var trainedClassifier = ee.Classifier.smileRandomForest(300).train({
features: trainingSample,
classProperty: label,
inputProperties: img.bandNames()
});

// Get information about the trained classifier.
print('Results of trained classifier', trainedClassifier.explain());

// Get a confusion matrix and overall accuracy for the training sample.
var trainAccuracy = trainedClassifier.confusionMatrix();
print('Training error matrix', trainAccuracy);
print('Training overall accuracy', trainAccuracy.accuracy());

// Get a confusion matrix and overall accuracy for the validation sample.
validationSample = validationSample.classify(trainedClassifier);
var validationAccuracy = validationSample.errorMatrix(label, 'classification');
print('Validation error matrix', validationAccuracy);
print('Validation accuracy', validationAccuracy.accuracy());

// Classify the reflectance image from the trained classifier.
var imgClassified = img.classify(trainedClassifier);

// Add the layers to the map.
var classVis = {
min: 0,
max: 10,
palette: ['006400' ,'b59475', '77a123', '9b7653', 'fa0000', 'c3d593',
            'f0f0f0', '0064c8', '0096a0', '00cf75', 'fae6a0']
};


Map.addLayer(img.clip(roi), {bands: ['B11', 'B8', 'B3'], min: 0, max: 0.5}, 'img');
Map.addLayer(lc.clip(roi), classVis, 'lc', false);
Map.addLayer(imgClassified.clip(roi), classVis, 'Classified');
Map.addLayer(roi, {color: 'white'}, 'ROI', false, 0.5);
Map.addLayer(trainingSample, {color: 'yellow'}, 'Training sample', false);
Map.addLayer(validationSample, {color: 'white'}, 'Validation sample', false);

//----------------------------- Display legend on the map --------------------------//
var panel = ui.Panel({
style: {
    position: 'bottom-left',
    padding: '5px;'
}
})

var title = ui.Label({
value: 'Classification',
style: {
    fontSize: '14px',
    fontWeight: 'bold',
    margin: '0px;'
}
})

panel.add(title)

var color = ['006400' ,'b59475', '77a123', '9b7653', 'fa0000', 'c3d593',
            'f0f0f0', '0064c8', '0096a0', '00cf75', 'fae6a0']
var lc_class = ['Trees', 'Shrubland', 'Grassland', 'Cropland', 'Built-up',
            'Barren / sparse vegetation', 'Snow and ice', 'Open water', 'Herbaceous wetland',
            'Mangroves', 'Moss and lichen']

var list_legend = function(color, description) {

var c = ui.Label({
    style: {
    backgroundColor: color,
    padding: '10px',
    margin: '4px'
    }
})

var ds = ui.Label({
    value: description,
    style: {
    margin: '5px'
    }
})

return ui.Panel({
    widgets: [c, ds],
    layout: ui.Panel.Layout.Flow('horizontal')
})
}

for(var a = 0; a < 11; a++){
panel.add(list_legend(color[a], lc_class[a]))
}

Map.add(panel)

Result in GEE App

Open in full screen