// 1.1 Unsupervised k-Means classification

// This function does unsupervised clustering classification

// input = any image. All bands will be used for clustering.

// numberOfUnsupervisedClusters = tunable parameter for how

// many clusters to create.

var afn_Kmeans = function(input,

numberOfUnsupervisedClusters,

defaultStudyArea, nativeScaleOfImage) {

// Make a new sample set on the input. Here the sample

// randomly selected spatially.

var training = input.sample({

region: defaultStudyArea,

scale: nativeScaleOfImage,

numPixels: 1000

});

var cluster = ee.Clusterer.wekaKMeans(

numberOfUnsupervisedClusters)

.train(training);

// Now apply that clusterer to the raw image that was also passed in. set is

var toexport = input.cluster(cluster);

// The first item is the unsupervised classification. Name the band.

var clusterUnsup = toexport.select(0).rename(

'unsupervisedClass');

return (clusterUnsup);

};

// 1.2 Simple normalization by maxes function.

var afn_normalize_by_maxes = function(img, bandMaxes) {

return img.divide(bandMaxes);

};

// 1.4 Simple add mean to Band Name function

var afn_addMeanToBandName = (function(i) {

return i + '_mean'; });

////////////////////////////////////////////////////////////

// 2. Parameters to function calls

////////////////////////////////////////////////////////////

// 2.1. Unsupervised KMeans Classification Parameters

var numberOfUnsupervisedClusters = 4;

////////////////////////////////////////////////////////////

// 2.2. Visualization and Saving parameters

// For different images, you might want to change the min and max

// values to stretch. Useful for images 2 and 3, the normalized images.

var centerObjectYN = true;

/////3.1 selecting image to classify

var whichImage = 1;

if (whichImage == 1){

var whichCollection = 'COPERNICUS/S2'

var ImageToUseID= '20160421T191704_20160421T212107_T10TDT';

var originalImage = ee.Image(whichCollection + '/' +ImageToUseID);

print(ImageToUseID, originalImage);

var nativeScaleOfImage = 10;

var threeBandsToDraw = ['B4', 'B3', 'B2'];

var bandsToUse = ['B4', 'B3', 'B2'];

var bandMaxes = [1e4, 1e4, 1e4];

var drawMin = 0

var drawMax = 0.3

var defaultStudyArea = ee.Geometry.Polygon(

[

[

[-123.13105468749993, 47.612974066532004],

[-123.13105468749993, 47.56214700543596],

[-123.00179367065422, 47.56214700543596],

[-123.00179367065422, 47.612974066532004]

]

]);

var zoomArea = ee.Geometry.Polygon(

[

[

[-123.13105468749993, 47.612974066532004],

[-123.13105468749993, 47.56214700543596],

[-123.00179367065422, 47.56214700543596],

[-123.00179367065422, 47.612974066532004]

]

], null,false);

}

Map.addLayer(originalImage.select(threeBandsToDraw), {

min: 0,

max: 2000

}, '3.1 '+ ImageToUseID, true, 1);

////////////////////////////////////////////////////////////

// 4. Image Preprocessing

////////////////////////////////////////////////////////////

var clippedImageSelectedBands =

originalImage.clip(defaultStudyArea)

.select(bandsToUse);

var ImageToUse =

afn_normalize_by_maxes(clippedImageSelectedBands,

bandMaxes);

Map.addLayer(ImageToUse.select(threeBandsToDraw), {

min: 0.028,

max: 0.12

},

'4.3 Pre-normalized image', true, 0);

////////////////////////////////////////////////////////////

// 6. Execute Classifications

////////////////////////////////////////////////////////////

// 6.1 Per Pixel Unsupervised Classification for Comparison

var PerPixelUnsupervised = afn_Kmeans(ImageToUse,

numberOfUnsupervisedClusters, defaultStudyArea,

nativeScaleOfImage);

Map.addLayer(PerPixelUnsupervised.select('unsupervisedClass'

)

.randomVisualizer(), {}, '6.1 Per-Pixel Unsupervised',

true, 0

);

print('6.1b Per-Pixel Unsupervised Results:',PerPixelUnsupervised);

////////////////////////////////////////////////////////////

// 7. Zoom if requested

////////////////////////////////////////////////////////////

if (centerObjectYN === true) {

Map.centerObject(zoomArea, 14); }

///////////////////////////////////

//Detecting Objects in Imagery with the SNIC

//Algorithm

////////////////////////////////

// 1.3 Seed Creation and SNIC segmentation Function

var afn_SNIC = function(imageOriginal, SuperPixelSize,

Compactness,

Connectivity, NeighborhoodSize, SeedShape) {

var theSeeds =

ee.Algorithms.Image.Segmentation.seedGrid(

SuperPixelSize, SeedShape);

var snic2 = ee.Algorithms.Image.Segmentation.SNIC({

image: imageOriginal,

size: SuperPixelSize,

compactness: Compactness,

connectivity: Connectivity,

neighborhoodSize: NeighborhoodSize,

seeds: theSeeds

});

var theStack = snic2.addBands(theSeeds);

return (theStack);};

// 2.3 Object-growing parameters to change

// Adjustable Superpixel Seed and SNIC segmentation Parameters:

// The superpixel seed location spacing, in pixels.

var SNIC_SuperPixelSize = 16;

// Larger values cause clusters to be more compact (square/hexagonal).

// Setting this to 0 disables spatial distance weighting.

var SNIC_Compactness = 0;

// Connectivity. Either 4 or 8.

var SNIC_Connectivity = 4;

// Either 'square' or 'hex'.

var SNIC_SeedShape = 'square';

// 2.4 Parameters that can stay unchanged

// Tile neighborhood size (to avoid tile boundary artifacts). Defaults to 2 * size.

var SNIC_NeighborhoodSize = 2 *SNIC_SuperPixelSize;

////////////////////////////////////////////////////////////

// 5. SNIC Clustering

////////////////////////////////////////////////////////////

// This function returns a multi-banded image that has had SNIC

// applied to it. It automatically determine the new names

// of the bands that will be returned from the segmentation.

print('5.1 Execute SNIC');

var SNIC_MultiBandedResults = afn_SNIC(

ImageToUse,

SNIC_SuperPixelSize,

SNIC_Compactness,

SNIC_Connectivity,

SNIC_NeighborhoodSize,

SNIC_SeedShape

);

var SNIC_MultiBandedResults = SNIC_MultiBandedResults

.reproject('EPSG:3857', null, nativeScaleOfImage);

print('5.2 SNIC Multi-Banded Results',

SNIC_MultiBandedResults);

Map.addLayer(SNIC_MultiBandedResults.select('clusters')

.randomVisualizer(), {}, '5.3 SNIC Segment Clusters',

true, 1);

var theSeeds = SNIC_MultiBandedResults.select('seeds');

Map.addLayer(theSeeds, {

palette: 'red'

}, '5.4 Seed points of clusters', true, 1);

var bandMeansToDraw =

threeBandsToDraw.map(afn_addMeanToBandName);

print('5.5 band means to draw', bandMeansToDraw);

var clusterMeans =

SNIC_MultiBandedResults.select(bandMeansToDraw);

print('5.6 Cluster Means by Band', clusterMeans);

Map.addLayer(clusterMeans, {

min: drawMin,

max: drawMax

}, '5.7 Image repainted by segments', true, 0);