Hands-on: Land Cover Classification with Optical & Radar Data

//-------------------------------------------------------------------
// Load the SAR data
//-------------------------------------------------------------------
// Load Sentinel-1 C-band SAR Ground Range collection (log scale, VV,descending)
var collectionVV = ee.ImageCollection('COPERNICUS/S1_GRD')
.filter(ee.Filter.eq('instrumentMode', 'IW'))
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))
.filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))
.filterMetadata('resolution_meters', 'equals' , 10)
.filterBounds(roi)
.select('VV');
print(collectionVV, 'Collection VV');

// Load Sentinel-1 C-band SAR Ground Range collection (log scale, VH, descending)
var collectionVH = ee.ImageCollection('COPERNICUS/S1_GRD')
.filter(ee.Filter.eq('instrumentMode', 'IW'))
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
.filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))
.filterMetadata('resolution_meters', 'equals' , 10)
.filterBounds(roi)
.select('VH');
print(collectionVH, 'Collection VH');

//Filter by date
var SARVV = collectionVV.filterDate('2019-08-01', '2019-08-10').mosaic();
var SARVH = collectionVH.filterDate('2019-08-01', '2019-08-10').mosaic();

// Add the SAR images to "layers" in order to display them
Map.centerObject(roi, 7);
Map.addLayer(SARVV, {min:-15,max:0}, 'SAR VV', 0);
Map.addLayer(SARVH, {min:-25,max:0}, 'SAR VH', 0);


//-------------------------------------------------------------------
// Load the optical data
//-------------------------------------------------------------------
// Function to cloud mask from the pixel QA band of Landsat 8 SR data.
function maskL8sr(image) {
// Bits 3 and 5 are cloud shadows and clouds, respectively.
var cloudShadowBitMask = 1 << 3;
var cloudsBitMask = 1 << 5;
// Get the pixel QA band.
var qa = image.select('pixel_qa');
// Both flags should be set to zero, indicating clear conditions.
var mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0)
.and(qa.bitwiseAnd(cloudsBitMask).eq(0));
// Return the masked image, scaled to reflectance, without the QA bands.
return image.updateMask(mask).divide(10000)
.select("B[0-9]*")
.copyProperties(image, ["system:time_start"]);
}

// Extract the images from the Landsat8 collection
var collectionl8 = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
.filterDate('2019-08-01', '2019-08-10')
.filterBounds(roi)
.map(maskL8sr);
print(collectionl8, 'Landsat');

//-------------------------------------------------------------------
// Generate NDVI images from the Landsat 8 data
//-------------------------------------------------------------------
//Calculate NDVI and create an image that contains all Landsat 8 bands and NDVI
var comp = collectionl8.mean();
var ndvi = comp.normalizedDifference(['B5', 'B4']).rename('NDVI');
var composite = ee.Image.cat(comp,ndvi);

// Add images to layers in order to display them
Map.centerObject(roi, 7);
Map.addLayer(composite, {bands: ['B4', 'B3', 'B2'], min: 0.1, max: 0.2}, 'Optical');
//change 0 to 0.1 upwards


//-------------------------------------------------------------------
// Apply speckle filter over the SAR data
//-------------------------------------------------------------------
//Apply filter to reduce speckle
var SMOOTHING_RADIUS = 50;
var SARVV_filtered = SARVV.focal_mean(SMOOTHING_RADIUS, 'circle', 'meters');
var SARVH_filtered = SARVH.focal_mean(SMOOTHING_RADIUS, 'circle', 'meters');

//Display the SAR filtered images
Map.addLayer(SARVV_filtered, {min:-15,max:0}, 'SAR VV Filtered',0);
Map.addLayer(SARVH_filtered, {min:-25,max:0}, 'SAR VH Filtered',0);

//-------------------------------------------------------------------
// Collect training data for supervised classification with Random Forest
//-------------------------------------------------------------------
//Merge Feature Collections
var newfc = open_water.merge(bare_fields).merge(vegetation1).merge(vegetation2).merge(vegetation3).merge(vegetation4).merge(forest);

//Define the SAR bands to train your data
var final = ee.Image.cat(SARVV_filtered,SARVH_filtered);
var bands = ['VH','VV'];
var training = final.select(bands).sampleRegions({
collection: newfc,
properties: ['landcover'],
scale: 30 });

//Train the classifier
var classifier = ee.Classifier.smileCart().train({
features: training,
classProperty: 'landcover',
inputProperties: bands
});

//Run the Classifier
var classified = final.select(bands).classify(classifier);

//Display the Classification
Map.addLayer(classified,
{min: 1, max: 7, palette: ['1667fa', 'c9270d', 'cf7b68', 'ee9a1c', '146d0e', '04bd23', '37fe05']},
'SAR Classification');

//-------------------------------------------------------------------
// Classify the Optical image: Supervised Classification with Random Forest
//-------------------------------------------------------------------
//Define the Landsat bands to train your data
var bandsl8 = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B10', 'B11', 'NDVI' ];
//var bandsl8 = ['NDVI' ];
var trainingl8 = composite.select(bandsl8).sampleRegions({
collection: newfc,
properties: ['landcover'],
scale: 30
});

//Train the classifier
var classifierl8 = ee.Classifier.smileCart().train({
features: trainingl8,
classProperty: 'landcover',
inputProperties: bandsl8
});

//Run the Classifier
var classifiedl8 = composite.select(bandsl8).classify(classifierl8);

//Display the Classification
Map.addLayer(classifiedl8,
{min: 1, max: 7, palette: ['1667fa', 'c9270d', 'cf7b68', 'ee9a1c', '146d0e', '04bd23', '37fe05']},
'Optical Classification');

//-------------------------------------------------------------------
// Assess the Accuracy of SAR-based classification
//-------------------------------------------------------------------
// Create a confusion matrix representing resubstitution accuracy.
print('RF- SAR error matrix: ', classifier.confusionMatrix());
print('RF- SAR accuracy: ', classifier.confusionMatrix().accuracy());

//-------------------------------------------------------------------
// Assess the Accuracy of Optical based classification
//-------------------------------------------------------------------
// Create a confusion matrix representing resubstitution accuracy.
print('RF-L8 error matrix: ', classifierl8.confusionMatrix());
print('RF-L8 accuracy: ', classifierl8.confusionMatrix().accuracy());

//-------------------------------------------------------------------
// Classification based on both Optical and SAR data
//-------------------------------------------------------------------
//Define both optical and SAR to train your data
var opt_sar = ee.Image.cat(composite, SARVV_filtered,SARVH_filtered);
var bands_opt_sar = ['VH','VV','B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B10', 'B11', 'NDVI'];
var training_opt_sar = opt_sar.select(bands_opt_sar).sampleRegions({
collection: newfc,
properties: ['landcover'],
scale: 30 });

//Train the classifier
var classifier_opt_sar = ee.Classifier.smileCart().train({
features: training_opt_sar,
classProperty: 'landcover',
inputProperties: bands_opt_sar
});

//Run the classifier
var classifiedboth = opt_sar.select(bands_opt_sar).classify(classifier_opt_sar);

//Display the Classification
Map.addLayer(classifiedboth,
{min: 1, max: 7, palette: ['1667fa', 'c9270d', 'cf7b68', 'ee9a1c', '146d0e', '04bd23', '37fe05']},
'Optical/SAR Classification');

//-------------------------------------------------------------------
// Assess the Accuracy of both Optical and SAR-based classification
//-------------------------------------------------------------------
// Create a confusion matrix representing resubstitution accuracy.
print('RF-Opt/SAR error matrix: ', classifier_opt_sar.confusionMatrix());
print('RF-Opt/SAR accuracy: ', classifier_opt_sar.confusionMatrix().accuracy());

//-------------------------------------------------------------------
// Export the finished classification as GeoTIFF
//-------------------------------------------------------------------
// remap values in class to integer above so class 0 becomes free for noData pixels   
var remap = ee.Image(classifiedboth)
 .remap([0,1,2,3,4,5,6,7], [1,2,3,4,5,6,7,8]);


print('remap',remap);
print(ee.List(classifiedboth));

// Get information about the bands as a list.
var bandNames = classifiedboth.bandNames();
print('Band names:', bandNames);  // ee.List of band names

// Get a list of all metadata properties.
var properties = classifiedboth.propertyNames();
print('Metadata properties:', properties);  // ee.List of metadata properties

var floatclass = classifiedboth.float;
print('flaot', floatclass);
 console.log(classifiedboth);


Export.image.toDrive({
image: classifiedboth.visualize({min: 1, max: 7, palette: ['1667fa', 'c9270d',  'cf7b68', 'ee9a1c',  '146d0e',  '04bd23',  '37fe05']}),
description: 'optical_SAR_classified',
scale: 500,
region: roi2,
fileFormat: 'GeoTIFF',
formatOptions: {
    cloudOptimized: true
  }
});

*/