land cover classification for gis mapping --> Google Earth Engine Code Editor JS API

src/landcover_classification.js

+var image = L8.filterBounds(roi).filterMetadata('CLOUD_COVER','less_than',3).filterDate('2017-01-01','2022-12-31').map(addNDVI).median().clip(roi);
+
+//var image2 = L7.filterBounds(roi_2).filterMetadata('CLOUD_COVER','less_than',1).filterDate('2019-01-01','2022-12-31')
+
+//var image_3 = S2.filterBounds(roi_2).filterMetadata('CLOUD_COVER','less_than',1).filterDate('2020-01-01','2022-12-31');
+
+
+
+
+//print(image);
+
+
+Map.addLayer(image,imageVisParam,'True Color Composite');
+Map.centerObject(roi,11.5);
+
+// Collect training samples
+
+// Merge training Samples
+
+var training = Urban.merge(Forest).merge(BareLand).merge(Water).merge(Agriculture);
+
+print(training);
+
+
+
+var label = 'Class';
+
+//when using external bands
+
+function addNDVI(image){
+ var ndvi =image.normalizedDifference(['B5','B4']).rename('NDVI');
+ return image.addBands(ndvi);
+}
+
+var bands = ['B1','B2','B3','B4','B5','B6','B7','NDVI'];
+
+var input = image.select(bands);
+
+// overlay the points on images to get training
+
+var train_image = input.sampleRegions({collection :training, properties:[label], scale:30});
+
+
+var train_data = train_image.randomColumn();
+
+var train_set = train_data.filter(ee.Filter.lessThan('random',0.8));
+var test_set = train_data.filter(ee.Filter.greaterThanOrEquals('random',0.2));
+
+
+// Classification model
+
+var classifier = ee.Classifier.smileCart().train(train_set,label,bands);
+
+// Classify the image 
+var classified = input.classify(classifier);
+
+
+// Define a color palette
+var colorPalette = ['FF3C33', '006837', '969696', '253494', 'FF8000'];
+
+Map.addLayer(
+  classified.clip(roi),
+  { palette: colorPalette, min: 0, max: 5 },
+  'CART Classification'
+);
+
+
+var train_accuracy = classifier.confusionMatrix();
+print(train_accuracy);
+
+// Get information about the trained classifier.
+print('Results of trained classifier', classifier.explain());
+
+// Get a confusion matrix and overall accuracy for the training sample.
+var train_accuracy = classifier.confusionMatrix();
+print('Training error matrix', train_accuracy);
+print('Training overall accuracy', train_accuracy.accuracy());
+
+
+var uniqueValues = classified.reduceRegion({
+  reducer: ee.Reducer.frequencyHistogram(),
+  geometry: roi,
+  scale: 30
+});
+
+print('Unique values:', uniqueValues.get('classification'));
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