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4. texCal
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4. texCal
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/* Author: Afag Rizayeva
Date: 12-26-2021
Purpose: Calculate second-order texture metrics from Corona imagery
Inputs: 2 Corona image layers manually imported in GEE
Instructions:
Delete the geometries that I used.
Adjust lines 14 and 16 according to the geometry we want to calculate by clipping them with the geometry cordinates (uncomment lines 15 and 17).
Press Ctrl+F twice. In Replace from cell paste: "1011-1040" and in Replace to cell paste your Corona images' code.
Do it once again without the hyphen.
Run the code first and create the appropriate geometries. (because the whole image is usually too large, but depends on your study. You may not need this step).
When exporting the layers make sure to add the geometry number at the end of the file and task names in the pop-up window
*/
// Import the images and if needed clip by geometry
var image2 = ee.Image('users/rizayeva/corona_2_5m/DS1011-1040DA_2_5m_JPEG_1band_EPSG32638');
// .clip(geometry1);
var image3 = ee.Image('users/rizayeva/corona_2_5m/DS1011-1040DF_2_5m_JPEG_1band_EPSG32638');
// .clip(geometry1);
// Build the image geometry
var geometry10111040 = image2.addBands(image3).geometry();
Map.addLayer(geometry10111040, {}, 'geometry10111040', false);
// Concatenate the two images
var img = ee.Image.cat([image2, image3]);
// print(img);
// Map.addLayer(table, {}, 'table', false);
Map.addLayer(img, {bands: ["ds10111040df", "ds10111040df", "ds10111040da"], gamma: 0.8}, 'DS1011-1040', false);
var bands = ['ds10111040da', 'ds10111040df'];
img = ee.Image(img).divide(255).select(bands);
Map.centerObject(geometry10111040, 13);
Map.addLayer(img, {gamma: 0.8}, 'RGBN', false);
///////////// Run segmentation
// Set the seed grid size
var seeds = ee.Algorithms.Image.Segmentation.seedGrid(40);
// Run SNIC segmentation algorithm on the regular square grid
var snic = ee.Algorithms.Image.Segmentation.SNIC({
image: img,
size: 32,
compactness: 0.1,
connectivity: 4,
neighborhoodSize:256,
seeds: seeds
}).select(['ds10111040da_mean', 'ds10111040df_mean', 'clusters'], ['ds10111040da', 'ds10111040df', 'clusters']).reproject({crs: 'EPSG:4326', scale: 18});
// Select clusters from segmentation results
var clusters = snic.select('clusters');
Map.addLayer(clusters, {}, 'clusters', false);
// Map.addLayer(clusters.randomVisualizer(), {}, 'clusters')
// Map.addLayer(snic, {bands: ["ds10111040df", "ds10111040df", "ds10111040da"], min:0, max:1, gamma: 0.8}, 'means', false);
// Create a cell grid across the image
var tiles = image2.addBands(image3).reduceToVectors({
reducer: ee.Reducer.mean(),
geometry: image2.addBands(image3).geometry(),
geometryType: 'bb',
scale: 12000,
eightConnected: false,
labelProperty: 'object',
maxPixels: 1e15
});
// print(tiles);
Map.addLayer(tiles, {}, 'tiles');
///////////// Create buffers for the cell grid across the image to prevent clipped segments later on
// Build the function
var buffer = function(feature) {
return feature.buffer(1000);
};
// Map the function
var tilesbuffer = tiles.map(buffer);
var tilesbuffer = ee.FeatureCollection(tilesbuffer);
// print('tilesbuffer', tilesbuffer);
Map.addLayer(tilesbuffer, {}, 'tilesbuffer');
// Get the total number of the cells across the image
var tilesbuffer_list = tilesbuffer.toList(tilesbuffer.size());
// print('tilesbuffer_list', tilesbuffer_list);
var size = tilesbuffer_list.size().getInfo();
///////////// Calculate texture for each object obtained as a result of segmentation
// Get the unique IDs for clusters
var freqHist = clusters.reduceRegion({
reducer: ee.Reducer.frequencyHistogram(),
geometry: geometry10111040,
scale: 2.5,
maxPixels: 1e13
});
// print(freqHist);
// print('first', ee.Dictionary(freqHist).first());
var obj = freqHist.get("clusters");
// print('obj', obj)
var keys = ee.Dictionary(obj).keys();
// print ('parse', ee.Number.parse(keys.get(0)));
// print ('keys', keys);
// Entropy DA
// Build a function to calculate entropy
var entropyDA = function(objctID) {
return ee.Image(image2.clip(tilesbuffer_list.get(i)).updateMask(clusters.eq(ee.Number.parse(objctID))).glcmTexture({size: 3}).select('ds10111040da_ent'));
};
// Apply the function to each object within the image for intra-class variability
var cliptile = [];
var i;
for (i = 0; i < size; i++) {
// var til = image2.clip(ee.Feature(tilesbuffer_list.get(i)).geometry());
var ds10111040da_ent = ee.ImageCollection((ee.Dictionary(clusters.reduceRegion({
reducer: ee.Reducer.frequencyHistogram(),
geometry: ee.Feature(tilesbuffer_list.get(i)).geometry(),
scale: 2.5,
maxPixels: 1e13
}).get("clusters")).keys()).map(entropyDA)).mosaic().toFloat();
cliptile[i]=ds10111040da_ent;
// print(cliptile)
// Map.addLayer(ee.Image(cliptile));
}
// Mosaic the resulting rasters
var cliptileMosaic = ee.ImageCollection(cliptile).mosaic();
// var cliptileMosaic = cliptileMosaic.addBands(clusters).reduceConnectedComponents(ee.Reducer.mean(), 'clusters', 256).select(['ds10111040da_ent'], ['ds10111040da_ent_mean']);
// print(cliptileMosaic);
// Map.addLayer(cliptileMosaic);
var cliptileMosaictoFloat = cliptileMosaic.toFloat();
// print(cliptileMosaictoFloat);
// Map.addLayer(cliptileMosaictoFloat);
// Export the resulting texture raster
Export.image.toAsset({
image: cliptileMosaictoFloat,
description: "entropy_1011-1040DA_7x7_geometry",
assetId: "GLCM_10111040/entropy_1011-1040DA_7x7_geometry",
scale: 2.5,
region: geometry10111040,
maxPixels:1e13,
});
// Entropy DF
// Build a function to calculate entropy
var entropyDF = function(objctID) {
return ee.Image(image3.clip(tilesbuffer_list.get(i)).updateMask(clusters.eq(ee.Number.parse(objctID))).glcmTexture({size: 3}).select('ds10111040df_ent'));
};
// Apply the function to each object within the image for intra-class variability
var cliptile = [];
var i;
for (i = 0; i < size; i++) {
// var til = image3.clip(ee.Feature(tilesbuffer_list.get(i)).geometry());
var ds10111040df_ent = ee.ImageCollection((ee.Dictionary(clusters.reduceRegion({
reducer: ee.Reducer.frequencyHistogram(),
geometry: ee.Feature(tilesbuffer_list.get(i)).geometry(),
scale: 2.5,
maxPixels: 1e13
}).get("clusters")).keys()).map(entropyDF)).mosaic().toFloat();
cliptile[i]=ds10111040df_ent;
// print(cliptile)
// Map.addLayer(ee.Image(cliptile));
}
// Mosaic the resulting rasters
var cliptileMosaic = ee.ImageCollection(cliptile).mosaic();
// var cliptileMosaic = cliptileMosaic.addBands(clusters).reduceConnectedComponents(ee.Reducer.mean(), 'clusters', 256).select(['ds10111040df_ent'], ['ds10111040df_ent_mean']);
// print(cliptileMosaic);
// Map.addLayer(cliptileMosaic);
var cliptileMosaictoFloat = cliptileMosaic.toFloat();
// print(cliptileMosaictoFloat);
// Map.addLayer(cliptileMosaictoFloat);
// Export the resulting texture raster
Export.image.toAsset({
image: cliptileMosaictoFloat,
description: "entropy_1011-1040DF_7x7_geometry",
assetId: "GLCM_10111040/entropy_1011-1040DF_7x7_geometry",
scale: 2.5,
region: geometry10111040,
maxPixels:1e13,
});
// Homogeneity DA
// Build a function to calculate homogeneity
var homogeneityDA = function(objctID) {
return ee.Image(image2.clip(tilesbuffer_list.get(i)).updateMask(clusters.eq(ee.Number.parse(objctID))).glcmTexture({size: 3}).select('ds10111040da_idm'));
};
// Apply the function to each object within the image for intra-class variability
var cliptile = [];
var i;
for (i = 0; i < size; i++) {
// var til = image2.clip(ee.Feature(tilesbuffer_list.get(i)).geometry());
var ds10111040da_idm = ee.ImageCollection((ee.Dictionary(clusters.reduceRegion({
reducer: ee.Reducer.frequencyHistogram(),
geometry: ee.Feature(tilesbuffer_list.get(i)).geometry(),
scale: 2.5,
maxPixels: 1e13
}).get("clusters")).keys()).map(homogeneityDA)).mosaic().toFloat();
cliptile[i]=ds10111040da_idm;
// print(cliptile)
// Map.addLayer(ee.Image(cliptile));
}
// Mosaic the resulting rasters
var cliptileMosaic = ee.ImageCollection(cliptile).mosaic();
// var cliptileMosaic = cliptileMosaic.addBands(clusters).reduceConnectedComponents(ee.Reducer.mean(), 'clusters', 256).select(['ds10111040df_ent'], ['ds10111040df_ent_mean']);
// print(cliptileMosaic);
// Map.addLayer(cliptileMosaic);
var cliptileMosaictoFloat = cliptileMosaic.toFloat();
// print(cliptileMosaictoFloat);
// Map.addLayer(cliptileMosaictoFloat);
// Export the resulting texture raster
Export.image.toAsset({
image: cliptileMosaictoFloat,
description: "homogeneity_1011-1040DA_7x7_geometry",
assetId: 'GLCM_10111040/homogeneity_1011-1040DA_7x7_geometry',
scale: 2.5,
region: geometry10111040,
maxPixels:1e13,
});
// Homogeneity DF
// Build a function to calculate homogeneity
var homogeneityDF = function(objctID) {
return ee.Image(image3.clip(tilesbuffer_list.get(i)).updateMask(clusters.eq(ee.Number.parse(objctID))).glcmTexture({size: 3}).select('ds10111040df_idm'));
};
// Apply the function to each object within the image for intra-class variability
var cliptile = [];
var i;
for (i = 0; i < size; i++) {
// var til = image3.clip(ee.Feature(tilesbuffer_list.get(i)).geometry());
var ds10111040df_idm = ee.ImageCollection((ee.Dictionary(clusters.reduceRegion({
reducer: ee.Reducer.frequencyHistogram(),
geometry: ee.Feature(tilesbuffer_list.get(i)).geometry(),
scale: 2.5,
maxPixels: 1e13
}).get("clusters")).keys()).map(homogeneityDF)).mosaic().toFloat();
cliptile[i]=ds10111040df_idm;
// print(cliptile)
// Map.addLayer(ee.Image(cliptile));
}
// Mosaic the resulting rasters
var cliptileMosaic = ee.ImageCollection(cliptile).mosaic();
// // var cliptileMosaic = cliptileMosaic.addBands(clusters).reduceConnectedComponents(ee.Reducer.mean(), 'clusters', 256).select(['ds10111040df_ent'], ['ds10111040df_ent_mean']);
// print(cliptileMosaic);
// Map.addLayer(cliptileMosaic);
var cliptileMosaictoFloat = cliptileMosaic.toFloat();
// // // print(cliptileMosaictoFloat);
// // Map.addLayer(cliptileMosaictoFloat);
// Export the resulting texture raster
Export.image.toAsset({
image: cliptileMosaictoFloat,
description: "homogeneity_1011-1040DF_7x7_geometry",
assetId: 'GLCM_10111040/homogeneity_1011-1040DF_7x7_geometry',
scale: 2.5,
region: geometry10111040,
maxPixels:1e13,
});
// Angular Second Moment DA
// Build a function to calculate angular second moment
var angularSecondMomentDA = function(objctID) {
return ee.Image(image2.clip(tilesbuffer_list.get(i)).updateMask(clusters.eq(ee.Number.parse(objctID))).glcmTexture({size: 3}).select('ds10111040da_asm'));
};
// Apply the function to each object within the image for intra-class variability
var cliptile = [];
var i;
for (i = 0; i < size; i++) {
// var til = image2.clip(ee.Feature(tilesbuffer_list.get(i)).geometry());
var ds10111040da_asm = ee.ImageCollection((ee.Dictionary(clusters.reduceRegion({
reducer: ee.Reducer.frequencyHistogram(),
geometry: ee.Feature(tilesbuffer_list.get(i)).geometry(),
scale: 2.5,
maxPixels: 1e13
}).get("clusters")).keys()).map(angularSecondMomentDA)).mosaic().toFloat();
cliptile[i]=ds10111040da_asm;
// print(cliptile)
// Map.addLayer(ee.Image(cliptile));
}
// Mosaic the resulting rasters
var cliptileMosaic = ee.ImageCollection(cliptile).mosaic();
// var cliptileMosaic = cliptileMosaic.addBands(clusters).reduceConnectedComponents(ee.Reducer.mean(), 'clusters', 256).select(['ds10111040da_ent'], ['ds10111040da_ent_mean']);
// print(cliptileMosaic);
// Map.addLayer(cliptileMosaic);
var cliptileMosaictoFloat = cliptileMosaic.toFloat();
// print(cliptileMosaictoFloat);
// Map.addLayer(cliptileMosaictoFloat);
// Export the resulting texture raster
Export.image.toAsset({
image: cliptileMosaictoFloat,
description: "angularSecondMoment_1011-1040DA_7x7_geometry",
assetId: "GLCM_10111040/angularSecondMoment_1011-1040DA_7x7_geometry",
scale: 2.5,
region: geometry10111040,
maxPixels:1e13,
});
// Angular Second Moment DF
// Build a function to calculate angular second moment
var angularSecondMomentDF = function(objctID) {
return ee.Image(image3.clip(tilesbuffer_list.get(i)).updateMask(clusters.eq(ee.Number.parse(objctID))).glcmTexture({size: 3}).select('ds10111040df_asm'));
};
// Apply the function to each object within the image for intra-class variability
var cliptile = [];
var i;
for (i = 0; i < size; i++) {
// var til = image3.clip(ee.Feature(tilesbuffer_list.get(i)).geometry());
var ds10111040df_asm = ee.ImageCollection((ee.Dictionary(clusters.reduceRegion({
reducer: ee.Reducer.frequencyHistogram(),
geometry: ee.Feature(tilesbuffer_list.get(i)).geometry(),
scale: 2.5,
maxPixels: 1e13
}).get("clusters")).keys()).map(angularSecondMomentDF)).mosaic().toFloat();
cliptile[i]=ds10111040df_asm;
// print(cliptile)
// Map.addLayer(ee.Image(cliptile));
}
// Mosaic the resulting rasters
var cliptileMosaic = ee.ImageCollection(cliptile).mosaic();
// var cliptileMosaic = cliptileMosaic.addBands(clusters).reduceConnectedComponents(ee.Reducer.mean(), 'clusters', 256).select(['ds10111040df_ent'], ['ds10111040df_ent_mean']);
// print(cliptileMosaic);
// Map.addLayer(cliptileMosaic);
var cliptileMosaictoFloat = cliptileMosaic.toFloat();
// print(cliptileMosaictoFloat);
// Map.addLayer(cliptileMosaictoFloat);
// Export the resulting texture raster
Export.image.toAsset({
image: cliptileMosaictoFloat,
description: "angularSecondMoment_1011-1040DF_7x7_geometry",
assetId: "GLCM_10111040/angularSecondMoment_1011-1040DF_7x7_geometry",
scale: 2.5,
region: geometry10111040,
maxPixels:1e13,
});