From 998969cc5b5e5806c06fbe628e534043690931f3 Mon Sep 17 00:00:00 2001
From: rizayeva <48497511+rizayeva@users.noreply.github.com>
Date: Mon, 24 Oct 2022 16:17:55 -0500
Subject: [PATCH] Delete 3. Pixel-based classificationO

---
 3. Pixel-based classificationO | 682 ---------------------------------
 1 file changed, 682 deletions(-)
 delete mode 100644 3. Pixel-based classificationO

diff --git a/3. Pixel-based classificationO b/3. Pixel-based classificationO
deleted file mode 100644
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--- a/3. Pixel-based classificationO	
+++ /dev/null
@@ -1,682 +0,0 @@
-/*  Author: Afag Rizayeva
-    Date: 01-17-2022
-    Purpose: Ten fold pixel-based classification of Corona data with incorporation of DEM
-    Inputs: Corona image geometry (study area polygon);
-            2 Corona image layers manually imported in GEE;
-            2.5m elevation layer;
-            training data directory;
-            testing data directory;
-
-    Adjust the input files' directories and palette variable
-    Run the code
-    Export the accuracies as csv files and the final classification map
-*/
-
-var table = ee.FeatureCollection('users/rizayeva/DS10111041');
-
-//     Set the palette based on number and names of classes
-var palette = ['075b0e'/* forest*/,  '075b0e'/* forest*/, '075b0e'/* forest*/, 'ebebeb'/* barren*/, '9de64e'/* grass*/, 'e2d544'/* crop*/, 'ff0000'/* urban*/, '3f553c'/* wetland*/, '0033ff'/* lake*/, 'ffffff'/* snow*/, '88cdf6'/* river*/];
-
-//     Import two Corona images
-//     Rename the band names for afterward- ('da') and forward-facing ('df') cameras
-var image2 = ee.Image('users/rizayeva/corona_2_5m/DS1011-1040DA_2_5m_JPEG_1band_EPSG32638').rename(['ds10111040da']);
-var image3 = ee.Image('users/rizayeva/corona_2_5m/DS1011-1040DF_2_5m_JPEG_1band_EPSG32638').rename(['ds10111040df']);
-Map.addLayer(image2, {min: 0, max: 200}, 'DS1011-1040DA', false);
-Map.addLayer(image3, {min: 0, max: 200}, 'DS1011-1040DF', false);
-
-//     Define and visualize the elevation layers
-var elevation = ee.Image('users/rizayeva/elevation').rename('elevation');
-var slope = ee.Terrain.slope(elevation).clip(table);
-var aspect = ee.Terrain.aspect(elevation).clip(table);
-var aspect = aspect.subtract(30).multiply(Math.PI/180).cos().subtract(1).multiply(-1/2);
-
-var imageVisParams = {bands: ['elevation'], min: 1, max: 3445,  gamma: [1.85], opacity: 1};
-Map.addLayer(elevation, imageVisParams, 'Elevation', false);
-Map.addLayer(slope, {}, 'slope', false);
-Map.addLayer(aspect, {}, 'aspect', false);
-
-var MN_DA = image2.reduceNeighborhood({
-  reducer: ee.Reducer.mean(),
-  kernel: ee.Kernel.square(3.5),
-});
-var MN_DF = image3.reduceNeighborhood({
-  reducer: ee.Reducer.mean(),
-  kernel: ee.Kernel.square(3.5),
-});
-var stdDevDA = image2.reduceNeighborhood({
-  reducer: ee.Reducer.stdDev(),
-  kernel: ee.Kernel.circle(3.5),
-});
-var stdDevDF = image3.reduceNeighborhood({
-  reducer: ee.Reducer.stdDev(),
-  kernel: ee.Kernel.circle(3.5),
-});
-
-// var texture = image2.glcmTexture({size: 4});
-// print(texture);
-var ENT_DA = image2.glcmTexture({size: 3/*4*/}).select('ds10111040da_ent');//.resample('bilinear').reproject({crs: image2.projection().getInfo()['crs'], scale: 16.5});
-var ENT_DF = image3.glcmTexture({size: 3/*4*/}).select('ds10111040df_ent');//.resample('bilinear').reproject({crs: image3.projection().getInfo()['crs'], scale: 16.5});
-var COR_DA = image2.glcmTexture({size: 3/*4*/}).select('ds10111040da_corr');//.resample('bilinear').reproject({crs: image2.projection().getInfo()['crs'], scale: 16.5});
-var COR_DF = image3.glcmTexture({size: 3/*4*/}).select('ds10111040df_corr');//.resample('bilinear').reproject({crs: image3.projection().getInfo()['crs'], scale: 16.5});
-var HOM_DA = image2.glcmTexture({size: 3/*4*/}).select('ds10111040da_idm');//.resample('bilinear').reproject({crs: image2.projection().getInfo()['crs'], scale: 16.5});
-var HOM_DF = image3.glcmTexture({size: 3/*4*/}).select('ds10111040df_idm');//.resample('bilinear').reproject({crs: image3.projection().getInfo()['crs'], scale: 16.5});
-var SM_DA = image2.glcmTexture({size: 3/*5*/}).select('ds10111040da_asm');//.resample('bilinear').reproject({crs: image2.projection().getInfo()['crs'], scale: 20.12});
-var SM_DF = image3.glcmTexture({size: 3/*5*/}).select('ds10111040df_asm');//.resample('bilinear').reproject({crs: image3.projection().getInfo()['crs'], scale: 20.12});
-var VAR_DA = image2.glcmTexture({size: 3/*5*/}).select('ds10111040da_var');//.resample('bilinear').reproject({crs: image2.projection().getInfo()['crs'], scale: 20.12});
-var VAR_DF = image3.glcmTexture({size: 3/*5*/}).select('ds10111040df_var');//.resample('bilinear').reproject({crs: image3.projection().getInfo()['crs'], scale: 20.12});
-var DIS_DA = image2.glcmTexture({size: 3/*5*/}).select('ds10111040da_diss');//.resample('bilinear').reproject({crs: image2.projection().getInfo()['crs'], scale: 20.12});
-var DIS_DF = image3.glcmTexture({size: 3/*5*/}).select('ds10111040df_diss');//.resample('bilinear').reproject({crs: image3.projection().getInfo()['crs'], scale: 20.12});
-Map.addLayer(MN_DA, {}, 'MN_DA', false);
-Map.addLayer(MN_DF, {}, 'MN_DF', false);
-Map.addLayer(ENT_DA, {}, 'ENT_DA', false);
-Map.addLayer(ENT_DF, {}, 'ENT_DF', false);
-Map.addLayer(SM_DA, {}, 'SM_DA', false);
-Map.addLayer(SM_DF, {}, 'SM_DF', false);
-Map.addLayer(COR_DA, {}, 'COR_DA', false);
-Map.addLayer(COR_DF, {}, 'COR_DF', false);
-Map.addLayer(HOM_DA, {}, 'HOM_DA', false);
-Map.addLayer(HOM_DF, {}, 'HOM_DF', false);
-Map.addLayer(VAR_DA, {}, 'VAR_DA', false);
-Map.addLayer(VAR_DF, {}, 'VAR_DF', false);
-Map.addLayer(DIS_DA, {}, 'DIS_DA', false);
-Map.addLayer(DIS_DF, {}, 'DIS_DF', false);
-
-//     Map asset directory that contains all Training and Testing data
-var assetIdTR='users/rizayeva/Training_Testing/Training10111040';
-var assetIdTE='users/rizayeva/Training_Testing/Testing10111040';
-
-//     Make a list that contains all the point data in the asset directory
-var assetListTR = ee.List(ee.data.getList({'id':assetIdTR}));
-var assetListTE = ee.List(ee.data.getList({'id':assetIdTE}));
-print('Training data', assetListTR);
-print('Testing data', assetListTE);
-//     Define the number of datasets
-var n = assetListTR.size().getInfo();
-
-///////////// Classification
-///////////// ///////////// ///////////// ///////////// ///////////// 5 TEX no DEM
-//     Concatenate all layers into one 
-var pixelPropertiesImage = ee.Image.cat([
-  MN_DA,
-  MN_DF,
-  stdDevDA,
-  stdDevDF,
-  ENT_DA,
-  ENT_DF,
-  HOM_DA,
-  HOM_DF,
-  SM_DA,
-  SM_DF
-  ]).float();
-// print (pixelPropertiesImage);
-var bandNames = pixelPropertiesImage.bandNames();
-print('pixelPropertiesImage bandNames with 5 textures no DEM', bandNames);
-var bandss = ['ds10111040da_mean', 'ds10111040df_mean', 'ds10111040da_stdDev', 'ds10111040df_stdDev', 'ds10111040da_asm', 'ds10111040df_asm', 'ds10111040da_ent', 'ds10111040df_ent', 'ds10111040da_idm', 'ds10111040df_idm'];
-
-//     Generate an empty Image Collection to later add each of the 10 classification results
-  var classification = ee.ImageCollection([]);
-//     Generate an empty list to later add each of the overall accuracies to be averaged
-  var overallAccuracy = ee.List([]);
-//     Build a for loop to run a Random Forest classifier using one set of the training and testing data each time
-  for (var i=0; i<n; i++){
-//     Select the subsequent set of training data each time
-    var valueTR=ee.Dictionary(assetListTR.get(i));
-    var imgIDTR=ee.String(valueTR.get('id')).getInfo();
-    var train=ee.FeatureCollection(imgIDTR);
-    // print('train', train);
-    // trainingTR=training.merge(train)
-
-//     Overlay the points on the imagery to get training.
-var training = pixelPropertiesImage.sampleRegions({
-  collection: train,
-  properties: ['Classes'],
-  scale: 2.5
-});
-
-//     Train a CART classifier with default parameters
-var classifier = ee.Classifier.smileRandomForest(100).train(training, 'Classes', bandss);
-
-//      Classify the image with the same bands used for training
-var classified = pixelPropertiesImage.classify(classifier);
-// print('classified', classified); 
-// Map.addLayer(classified, {min: 1, max: 11, palette: palette}, 'Classification map with 5 textures no DEM' + (i+1), false);
-
-// //     Export the Image - uncomment if want to export each
-// Export.image.toAsset({
-//   image: classified.toByte(),//.toFloat(),
-//   description: "classification_pixel_5TEXnoDEM" + (i+1),
-//   assetId: "classification_pixel_5TEXnoDEM" + (i+1),
-//   scale: 2.5,
-//   region: geometry10111040,
-//   maxPixels:1e13,
-// });
-
-//     Overall map
-classification=classification.merge(classified);
-
-//     Validation
-//     Select the subsequent set of testing data each time
-  var valueTE=ee.Dictionary(assetListTE.get(i));
-  var imgIDTE=ee.String(valueTE.get('id')).getInfo();
-  var test=ee.FeatureCollection(imgIDTE);
-  // print('test', test);
-  // testing=testing.merge(test)
-
-var validation = pixelPropertiesImage.sampleRegions({
-  collection: test,
-  properties: ['Classes'],
-  scale: 2.5
-});
-var validated = validation.classify(classifier);
-
-//     Get a confusion matrix representing expected accuracy and an overall accuracy
-//     Uncomment the "print" commands if want to visualize the accuracies and matrices for each set of training and testing data in GEE console
-var testAccuracy = validated.errorMatrix('Classes', 'classification');
-var Matrix = ee.Feature(null, {matrix: testAccuracy.array()});
-// print('Validation error matrix with 5 textures no DEM' + (i+1), testAccuracy);
-var overall = testAccuracy.accuracy();
-// print('Overall' + (i+1), overall);
-
-// //    Export the matrix - uncomment if want to export each
-// Export.table.toDrive({
-//   collection: ee.FeatureCollection(Matrix),
-//   description:'testAccuracy_pixel08122021_wDEM' + (i+1),
-//   folder: 'testAccuracy',
-//   fileFormat: 'csv'
-// });
-
-//     Add subsequent overall accuracy with each loop, to initially empty "overallAccuracy" list
-overallAccuracy = overallAccuracy.add(overall);
-
-  }
-
-//     Image collection of classification results from each set of training data
-var classifiedX = classification;
-// print('Classification maps collection', classifiedX);
-
-//     Best map representing the most common pixel value based on all sets of training data
-var bestClassificationPxl = classifiedX.mode();
-
-//     The following map may not load due to memory limits, but should succesfully export to Assets or Drive
-Map.addLayer(bestClassificationPxl.toByte(), {min: 1, max: 11, palette: palette}, "Best pixel-based classification with 5 textures no DEM", false);
-
-//     Export the best map to Drive
-Export.image.toDrive({
-  image: bestClassificationPxl.toByte(),
-  description: "Pxl_5TEXnoDEM",
-  folder: "pixelBasedClassifications",
-  scale: 2.5,
-  region: table,
-  maxPixels:1e13,
-});
-
-//     Calculate the average overall accuracy
-var overallAccuracyPxl = ee.FeatureCollection(overallAccuracy.reduce(ee.Reducer.mean()));
-print('Overall accuracy with 5 textures no DEM', overallAccuracyPxl);
-
-//     Calculate the Standard Deviation based on overall accuracies from all training data
-var standardDeviation = ee.FeatureCollection(overallAccuracy.reduce(ee.Reducer.stdDev()));
-print('SD with 5 textures no DEM', standardDeviation);
-
-//     The "print" function may not always work due to the
-//     To make sure it works, we can reduce the number of training and testing datasets used
-//     For example, use just two datasets (2 instead of n in the beginning of the for loop)
-//     Otherwise, it should always be possible to export them to Drive
-Export.table.toDrive({
-  collection: overallAccuracyPxl,
-  description:'overallAccuracyPxl5TEXnoDEM10111040',
-  folder: 'classification_accuracies',
-  fileFormat: 'csv'
-});
-
-Export.table.toDrive({
-  collection: standardDeviation,
-  description:'standardDeviationPxl5TEXnoDEM10111040',
-  folder: 'classification_accuracies',
-  fileFormat: 'csv'
-});
-
-///////////// ///////////// ///////////// ///////////// ///////////// 8 TEX no DEM
-//     Concatenate all layers into one 
-var pixelPropertiesImage = ee.Image.cat([
-  MN_DA,
-  MN_DF,
-  stdDevDA,
-  stdDevDF,
-  ENT_DA,
-  ENT_DF,
-  HOM_DA,
-  HOM_DF,
-  SM_DA,
-  SM_DF,
-  VAR_DA,
-  VAR_DF,
-  DIS_DA,
-  DIS_DF,
-  COR_DA,
-  COR_DF
-  ]).float();
-// print (pixelPropertiesImage);
-var bandNames = pixelPropertiesImage.bandNames();
-print('pixelPropertiesImage bandNames with 8 textures no DEM', bandNames);
-var bandss = ['ds10111040da_mean', 'ds10111040df_mean', 'ds10111040da_stdDev', 'ds10111040df_stdDev', 'ds10111040da_asm', 'ds10111040df_asm', 'ds10111040da_var', 'ds10111040df_var', 'ds10111040da_diss', 'ds10111040df_diss', 'ds10111040da_ent', 'ds10111040df_ent', 'ds10111040da_corr', 'ds10111040df_corr', 'ds10111040da_idm', 'ds10111040df_idm'];
-
-//     Generate an empty Image Collection to later add each of the 10 classification results
-  var classification = ee.ImageCollection([]);
-//     Generate an empty list to later add each of the overall accuracies to be averaged
-  var overallAccuracy = ee.List([]);
-//     Build a for loop to run a Random Forest classifier using one set of the training and testing data each time
-  for (var i=0; i<n; i++){
-//     Select the subsequent set of training data each time
-    var valueTR=ee.Dictionary(assetListTR.get(i));
-    var imgIDTR=ee.String(valueTR.get('id')).getInfo();
-    var train=ee.FeatureCollection(imgIDTR);
-    // print('train', train);
-    // trainingTR=training.merge(train)
-
-//     Overlay the points on the imagery to get training.
-var training = pixelPropertiesImage.sampleRegions({
-  collection: train,
-  properties: ['Classes'],
-  scale: 2.5
-});
-
-//     Train a CART classifier with default parameters
-var classifier = ee.Classifier.smileRandomForest(100).train(training, 'Classes', bandss);
-
-//      Classify the image with the same bands used for training
-var classified = pixelPropertiesImage.classify(classifier);
-// print('classified', classified); 
-// Map.addLayer(classified, {min: 1, max: 11, palette: palette}, 'Classification map with 8 textures no DEM' + (i+1), false);
-
-// //     Export the Image - uncomment if want to export each
-// Export.image.toAsset({
-//   image: classified.toByte(),//.toFloat(),
-//   description: "classification_pixel_8TEXnoDEM" + (i+1),
-//   assetId: "classification_pixel_8TEXnoDEM" + (i+1),
-//   scale: 2.5,
-//   region: geometry10111040,
-//   maxPixels:1e13,
-// });
-
-//     Overall map
-classification=classification.merge(classified);
-
-//     Validation
-//     Select the subsequent set of testing data each time
-  var valueTE=ee.Dictionary(assetListTE.get(i));
-  var imgIDTE=ee.String(valueTE.get('id')).getInfo();
-  var test=ee.FeatureCollection(imgIDTE);
-  // print('test', test);
-  // testing=testing.merge(test)
-
-var validation = pixelPropertiesImage.sampleRegions({
-  collection: test,
-  properties: ['Classes'],
-  scale: 2.5
-});
-var validated = validation.classify(classifier);
-
-//     Get a confusion matrix representing expected accuracy and an overall accuracy
-//     Uncomment the "print" commands if want to visualize the accuracies and matrices for each set of training and testing data in GEE console
-var testAccuracy = validated.errorMatrix('Classes', 'classification');
-var Matrix = ee.Feature(null, {matrix: testAccuracy.array()});
-// print('Validation error matrix with 8 textures no DEM' + (i+1), testAccuracy);
-var overall = testAccuracy.accuracy();
-// print('Overall' + (i+1), overall);
-
-// //    Export the matrix - uncomment if want to export each
-// Export.table.toDrive({
-//   collection: ee.FeatureCollection(Matrix),
-//   description:'testAccuracy_pixel08122021_wDEM' + (i+1),
-//   folder: 'testAccuracy',
-//   fileFormat: 'csv'
-// });
-
-//     Add subsequent overall accuracy with each loop, to initially empty "overallAccuracy" list
-overallAccuracy = overallAccuracy.add(overall);
-
-  }
-
-//     Image collection of classification results from each set of training data
-var classifiedX = classification;
-// print('Classification maps collection', classifiedX);
-
-//     Best map representing the most common pixel value based on all sets of training data
-var bestClassificationPxl = classifiedX.mode();
-
-//     The following map may not load due to memory limits, but should succesfully export to Assets or Drive
-Map.addLayer(bestClassificationPxl.toByte(), {min: 1, max: 11, palette: palette}, "Best pixel-based classification with 8 textures no DEM", false);
-
-//     Export the best map to Drive
-Export.image.toDrive({
-  image: bestClassificationPxl.toByte(),
-  description: "Pxl_8TEXnoDEM",
-  folder: "pixelBasedClassifications",
-  scale: 2.5,
-  region: table,
-  maxPixels:1e13,
-});
-
-//     Calculate the average overall accuracy
-var overallAccuracyPxl = ee.FeatureCollection(overallAccuracy.reduce(ee.Reducer.mean()));
-print('Overall accuracy with 8 textures no DEM', overallAccuracyPxl);
-
-//     Calculate the Standard Deviation based on overall accuracies from all training data
-var standardDeviation = ee.FeatureCollection(overallAccuracy.reduce(ee.Reducer.stdDev()));
-print('SD with 8 textures no DEM', standardDeviation);
-
-//     The "print" function may not always work due to the
-//     To make sure it works, we can reduce the number of training and testing datasets used
-//     For example, use just two datasets (2 instead of n in the beginning of the for loop)
-//     Otherwise, it should always be possible to export them to Drive
-Export.table.toDrive({
-  collection: overallAccuracyPxl,
-  description:'overallAccuracyPxl8TEXnoDEM10111040',
-  folder: 'classification_accuracies',
-  fileFormat: 'csv'
-});
-
-Export.table.toDrive({
-  collection: standardDeviation,
-  description:'standardDeviationPxl8TEXnoDEM10111040',
-  folder: 'classification_accuracies',
-  fileFormat: 'csv'
-});
-
-///////////// ///////////// ///////////// ///////////// ///////////// 5 TEX w DEM
-//     Concatenate all layers into one 
-var pixelPropertiesImage = ee.Image.cat([
-  MN_DA,
-  MN_DF,
-  stdDevDA,
-  stdDevDF,
-  ENT_DA,
-  ENT_DF,
-  HOM_DA,
-  HOM_DF,
-  SM_DA,
-  SM_DF,
-  elevation,
-  slope,
-  aspect
-  ]).float();
-// print (pixelPropertiesImage);
-var bandNames = pixelPropertiesImage.bandNames();
-print('pixelPropertiesImage bandNames with 5 textures with DEM', bandNames);
-var bandss = ['ds10111040da_mean', 'ds10111040df_mean', 'ds10111040da_stdDev', 'ds10111040df_stdDev', 'ds10111040da_asm', 'ds10111040df_asm', 'ds10111040da_ent', 'ds10111040df_ent', 'ds10111040da_idm', 'ds10111040df_idm', 'elevation', 'slope', 'aspect'];
-
-//     Generate an empty Image Collection to later add each of the 10 classification results
-  var classification = ee.ImageCollection([]);
-//     Generate an empty list to later add each of the overall accuracies to be averaged
-  var overallAccuracy = ee.List([]);
-//     Build a for loop to run a Random Forest classifier using one set of the training and testing data each time
-  for (var i=0; i<n; i++){
-//     Select the subsequent set of training data each time
-    var valueTR=ee.Dictionary(assetListTR.get(i));
-    var imgIDTR=ee.String(valueTR.get('id')).getInfo();
-    var train=ee.FeatureCollection(imgIDTR);
-    // print('train', train);
-    // trainingTR=training.merge(train)
-
-//     Overlay the points on the imagery to get training.
-var training = pixelPropertiesImage.sampleRegions({
-  collection: train,
-  properties: ['Classes'],
-  scale: 2.5
-});
-
-//     Train a CART classifier with default parameters
-var classifier = ee.Classifier.smileRandomForest(100).train(training, 'Classes', bandss);
-
-//      Classify the image with the same bands used for training
-var classified = pixelPropertiesImage.classify(classifier);
-// print('classified', classified); 
-// Map.addLayer(classified, {min: 1, max: 11, palette: palette}, 'Classification map with 5 textures with DEM' + (i+1), false);
-
-// //     Export the Image - uncomment if want to export each
-// Export.image.toAsset({
-//   image: classified.toByte(),//.toFloat(),
-//   description: "classification_pixel_5TEXwDEM" + (i+1),
-//   assetId: "classification_pixel_5TEXwDEM" + (i+1),
-//   scale: 2.5,
-//   region: geometry10111040,
-//   maxPixels:1e13,
-// });
-
-//     Overall map
-classification=classification.merge(classified);
-
-//     Validation
-//     Select the subsequent set of testing data each time
-  var valueTE=ee.Dictionary(assetListTE.get(i));
-  var imgIDTE=ee.String(valueTE.get('id')).getInfo();
-  var test=ee.FeatureCollection(imgIDTE);
-  // print('test', test);
-  // testing=testing.merge(test)
-
-var validation = pixelPropertiesImage.sampleRegions({
-  collection: test,
-  properties: ['Classes'],
-  scale: 2.5
-});
-var validated = validation.classify(classifier);
-
-//     Get a confusion matrix representing expected accuracy and an overall accuracy
-//     Uncomment the "print" commands if want to visualize the accuracies and matrices for each set of training and testing data in GEE console
-var testAccuracy = validated.errorMatrix('Classes', 'classification');
-var Matrix = ee.Feature(null, {matrix: testAccuracy.array()});
-// print('Validation error matrix with 5 textures with DEM' + (i+1), testAccuracy);
-var overall = testAccuracy.accuracy();
-// print('Overall' + (i+1), overall);
-
-// //    Export the matrix - uncomment if want to export each
-// Export.table.toDrive({
-//   collection: ee.FeatureCollection(Matrix),
-//   description:'testAccuracy_pixel08122021_wDEM' + (i+1),
-//   folder: 'testAccuracy',
-//   fileFormat: 'csv'
-// });
-
-//     Add subsequent overall accuracy with each loop, to initially empty "overallAccuracy" list
-overallAccuracy = overallAccuracy.add(overall);
-
-  }
-
-//     Image collection of classification results from each set of training data
-var classifiedX = classification;
-// print('Classification maps collection', classifiedX);
-
-//     Best map representing the most common pixel value based on all sets of training data
-var bestClassificationPxl = classifiedX.mode();
-
-//     The following map may not load due to memory limits, but should succesfully export to Assets or Drive
-Map.addLayer(bestClassificationPxl.toByte(), {min: 1, max: 11, palette: palette}, "Best pixel-based classification with 5 textures with DEM", false);
-
-//     Export the best map to Drive
-Export.image.toDrive({
-  image: bestClassificationPxl.toByte(),
-  description: "Pxl_5TEXwDEM",
-  folder: "pixelBasedClassifications",
-  scale: 2.5,
-  region: table,
-  maxPixels:1e13,
-});
-
-//     Calculate the average overall accuracy
-var overallAccuracyPxl = ee.FeatureCollection(overallAccuracy.reduce(ee.Reducer.mean()));
-print('Overall accuracy with 5 textures with DEM', overallAccuracyPxl);
-
-//     Calculate the Standard Deviation based on overall accuracies from all training data
-var standardDeviation = ee.FeatureCollection(overallAccuracy.reduce(ee.Reducer.stdDev()));
-print('SD with 5 textures with DEM', standardDeviation);
-
-//     The "print" function may not always work due to the
-//     To make sure it works, we can reduce the number of training and testing datasets used
-//     For example, use just two datasets (2 instead of n in the beginning of the for loop)
-//     Otherwise, it should always be possible to export them to Drive
-Export.table.toDrive({
-  collection: overallAccuracyPxl,
-  description:'overallAccuracyPxl5TEXwDEM10111040',
-  folder: 'classification_accuracies',
-  fileFormat: 'csv'
-});
-
-Export.table.toDrive({
-  collection: standardDeviation,
-  description:'standardDeviationPxl5TEXwDEM10111040',
-  folder: 'classification_accuracies',
-  fileFormat: 'csv'
-});
-
-///////////// ///////////// ///////////// ///////////// ///////////// 8 TEX w DEM
-//     Concatenate all layers into one 
-var pixelPropertiesImage = ee.Image.cat([
-  MN_DA,
-  MN_DF,
-  stdDevDA,
-  stdDevDF,
-  ENT_DA,
-  ENT_DF,
-  HOM_DA,
-  HOM_DF,
-  SM_DA,
-  SM_DF,
-  VAR_DA,
-  VAR_DF,
-  DIS_DA,
-  DIS_DF,
-  COR_DA,
-  COR_DF,
-  elevation,
-  slope,
-  aspect
-  ]).float();
-// print (pixelPropertiesImage);
-var bandNames = pixelPropertiesImage.bandNames();
-print('pixelPropertiesImage bandNames with 8 textures with DEM', bandNames);
-var bandss = ['ds10111040da_mean', 'ds10111040df_mean', 'ds10111040da_stdDev', 'ds10111040df_stdDev', 'ds10111040da_asm', 'ds10111040df_asm', 'ds10111040da_var', 'ds10111040df_var', 'ds10111040da_diss', 'ds10111040df_diss', 'ds10111040da_ent', 'ds10111040df_ent', 'ds10111040da_corr', 'ds10111040df_corr', 'ds10111040da_idm', 'ds10111040df_idm', 'elevation', 'slope', 'aspect'];
-
-//     Generate an empty Image Collection to later add each of the 10 classification results
-  var classification = ee.ImageCollection([]);
-//     Generate an empty list to later add each of the overall accuracies to be averaged
-  var overallAccuracy = ee.List([]);
-//     Build a for loop to run a Random Forest classifier using one set of the training and testing data each time
-  for (var i=0; i<n; i++){
-//     Select the subsequent set of training data each time
-    var valueTR=ee.Dictionary(assetListTR.get(i));
-    var imgIDTR=ee.String(valueTR.get('id')).getInfo();
-    var train=ee.FeatureCollection(imgIDTR);
-    // print('train', train);
-    // trainingTR=training.merge(train)
-
-//     Overlay the points on the imagery to get training.
-var training = pixelPropertiesImage.sampleRegions({
-  collection: train,
-  properties: ['Classes'],
-  scale: 2.5
-});
-
-//     Train a CART classifier with default parameters
-var classifier = ee.Classifier.smileRandomForest(100).train(training, 'Classes', bandss);
-
-//      Classify the image with the same bands used for training
-var classified = pixelPropertiesImage.classify(classifier);
-// print('classified', classified); 
-// Map.addLayer(classified, {min: 1, max: 11, palette: palette}, 'Classification map with 8 textures with DEM' + (i+1), false);
-
-// //     Export the Image - uncomment if want to export each
-// Export.image.toAsset({
-//   image: classified.toByte(),//.toFloat(),
-//   description: "classification_pixel_8TEXwDEM" + (i+1),
-//   assetId: "classification_pixel_8TEXwDEM" + (i+1),
-//   scale: 2.5,
-//   region: geometry10111040,
-//   maxPixels:1e13,
-// });
-
-//     Overall map
-classification=classification.merge(classified);
-
-//     Validation
-//     Select the subsequent set of testing data each time
-  var valueTE=ee.Dictionary(assetListTE.get(i));
-  var imgIDTE=ee.String(valueTE.get('id')).getInfo();
-  var test=ee.FeatureCollection(imgIDTE);
-  // print('test', test);
-  // testing=testing.merge(test)
-
-var validation = pixelPropertiesImage.sampleRegions({
-  collection: test,
-  properties: ['Classes'],
-  scale: 2.5
-});
-var validated = validation.classify(classifier);
-
-//     Get a confusion matrix representing expected accuracy and an overall accuracy
-//     Uncomment the "print" commands if want to visualize the accuracies and matrices for each set of training and testing data in GEE console
-var testAccuracy = validated.errorMatrix('Classes', 'classification');
-var Matrix = ee.Feature(null, {matrix: testAccuracy.array()});
-// print('Validation error matrix with 8 textures with DEM' + (i+1), testAccuracy);
-var overall = testAccuracy.accuracy();
-// print('Overall' + (i+1), overall);
-
-// //    Export the matrix - uncomment if want to export each
-// Export.table.toDrive({
-//   collection: ee.FeatureCollection(Matrix),
-//   description:'testAccuracy_pixel08122021_wDEM' + (i+1),
-//   folder: 'testAccuracy',
-//   fileFormat: 'csv'
-// });
-
-//     Add subsequent overall accuracy with each loop, to initially empty "overallAccuracy" list
-overallAccuracy = overallAccuracy.add(overall);
-
-  }
-
-//     Image collection of classification results from each set of training data
-var classifiedX = classification;
-// print('Classification maps collection', classifiedX);
-
-//     Best map representing the most common pixel value based on all sets of training data
-var bestClassificationPxl = classifiedX.mode();
-
-//     The following map may not load due to memory limits, but should succesfully export to Assets or Drive
-Map.addLayer(bestClassificationPxl.toByte(), {min: 1, max: 11, palette: palette}, "Best pixel-based classification with 8 textures with DEM", false);
-
-//     Export the best map to Drive
-Export.image.toDrive({
-  image: bestClassificationPxl.toByte(),
-  description: "Pxl_8TEXwDEM",
-  folder: "pixelBasedClassifications",
-  scale: 2.5,
-  region: table,
-  maxPixels:1e13,
-});
-
-//     Calculate the average overall accuracy
-var overallAccuracyPxl = ee.FeatureCollection(overallAccuracy.reduce(ee.Reducer.mean()));
-print('Overall accuracy with 8 textures with DEM', overallAccuracyPxl);
-
-//     Calculate the Standard Deviation based on overall accuracies from all training data
-var standardDeviation = ee.FeatureCollection(overallAccuracy.reduce(ee.Reducer.stdDev()));
-print('SD with 8 textures with DEM', standardDeviation);
-
-//     The "print" function may not always work due to the
-//     To make sure it works, we can reduce the number of training and testing datasets used
-//     For example, use just two datasets (2 instead of n in the beginning of the for loop)
-//     Otherwise, it should always be possible to export them to Drive
-Export.table.toDrive({
-  collection: overallAccuracyPxl,
-  description:'overallAccuracyPxl8TEXwDEM10111040',
-  folder: 'classification_accuracies',
-  fileFormat: 'csv'
-});
-
-Export.table.toDrive({
-  collection: standardDeviation,
-  description:'standardDeviationPxl8TEXwDEM10111040',
-  folder: 'classification_accuracies',
-  fileFormat: 'csv'
-});
-