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Create classification_algorithms
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@matthias92schubert
matthias92schubert authored Jan 7, 2020
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// Loading external scripts.
var altering_IC_algorithms = require('users/albremoteforest/DontDelete:altering_IC_algorithms');


// Function that automatically adapts the band_model to the available bands for a given multitemporal image.
var automatic_band_model_adjust = function(func_mt_image, func_band_model1) {
var list_number_of_bands = ee.List.sequence(0, func_band_model1.length().subtract(1));

// Analyse MT - image for classification regarding available bands.
var band_names = func_mt_image.bandNames();
var length_band_names = ee.Number(band_names.length());
var letzter_eintrag = ee.String(band_names.get(length_band_names.subtract(1)));
var index_ = letzter_eintrag.index("_");

// Function that actually builds the custom band model.
var construct_band_model_custom = function(last_item, index_of_, func_bands) {
// Extracts the number of single images used in constructing the multitemporal image.
var einzelbilder = ee.Number.parse(letzter_eintrag.slice(index_.add(1), index_.add(2)));
var list_number_of_images = ee.List.sequence(1, einzelbilder);
// Prepare band_model to match bands of image for classification.
// Construct band names (band names for classification + _ + number of single pictures in multitemporal image.
var band_model1 = list_number_of_images.map(function(number) {
number = ee.Number(number).toInt();
number = ee.String(number);
var wrap = func_bands.map(function(band) {
band = ee.String(band);
var band_plus_number = band.cat("_").cat(number);
return band_plus_number;
});
return wrap;
});

// Append all lists together.
var final_list = list_number_of_images.iterate(
function(instance, list) {
instance = ee.Number(instance).subtract(1);
list = ee.List(list);
var temp_list = list.add(band_model1.get(instance));
return temp_list;
},
ee.List(func_bands)
);
return ee.List(final_list).flatten();
};

// If-statement, activates function construct_band_model_custom only if a multitemporal image is in use.
// Otherwise original band_model is assumed.
var band_model_custom = ee.Algorithms.If(
ee.Algorithms.IsEqual(index_, -1),
band_model_custom = func_band_model1,
band_model_custom = construct_band_model_custom(letzter_eintrag, index_, func_band_model1)
);

return band_model_custom;
};


// Function that classifies one image, based on the training data passed to this function and a Random Forest.
var classifyImage = function(func_image, func_training_samples, func_band_model) {
func_image = ee.Image(func_image);
func_training_samples = ee.FeatureCollection(func_training_samples);
var training_properties = func_training_samples.get('classification_feature');
var func_band_model_custom = automatic_band_model_adjust(func_image, func_band_model);

// Sampling training data.
var training = func_image.select(func_band_model_custom).sampleRegions({
collection: func_training_samples,
properties: [training_properties],
scale: 10
});

// Train classifier.
var classifier = ee.Classifier.randomForest(500, 0).train({
features: training,
classProperty: training_properties,
inputProperties: func_band_model_custom
});

// Classify image.
var classified_image = func_image.select(func_band_model_custom).classify(classifier);

// Explain you, classifier!
var clas_explain = classifier.explain();

// Combine classified picture and explain into dictionary for return.
var dic_img_expl = ee.Dictionary({
'classified_image': classified_image,
'explanation': clas_explain
});

return classified_image;
//return dic_img_expl;
};


// Function that classifies one image, based on the training data passed to this function and a Random Forest,
// but returns the probability of the classification.
var classification_probability = function (func_image, func_training_samples, func_band_model) {
func_image = ee.Image(func_image);
func_training_samples = ee.FeatureCollection(func_training_samples);
var training_properties = func_training_samples.get('classification_feature');
var func_band_model_custom = automatic_band_model_adjust(func_image, func_band_model);

// Sampling training data.
var training = func_image.select(func_band_model_custom).sampleRegions({
collection: func_training_samples,
properties: [training_properties],
scale: 10
});

// Train classifier.
var classifier = ee.Classifier.randomForest(500, 0).setOutputMode('PROBABILITY').train({
features: training,
classProperty: training_properties,
inputProperties: func_band_model_custom
});

// Classify image.
var classified_image = func_image.select(func_band_model_custom).classify(classifier);

return classified_image;
};


// Function that takes a year, loads predefined images, combines them into one and classifies them in two steps.
// First deciduous and coniferous, then ash, beech and mixed forest. This basic forest classification is used to compare
// single-date classifiactions with.
var classifyForestMain = function(year, sample_data) {
year = ee.String(year);
sample_data = ee.Dictionary(sample_data);
var sample_data_classification_step_2 = ee.FeatureCollection(sample_data.get('dec_vs_con'));
var sample_data_classification_step_3 = ee.FeatureCollection(sample_data.get('ash_vs_beech'));
var bands_step_2 = ee.List(['B3', 'B4', 'B8']);
var bands_step_3 = ee.List(['B3', 'B4', 'B8']);
var func_bands = ee.List(['B2', 'B3', 'B8', 'EVI', 'NDVI']);
var forest_mask = ee.Image('users/albremoteforest/forest_masks_2016---2019')
.select(ee.String('FM_').cat(year))
.focal_min({kernel: ee.Kernel.circle(1)}).focal_max({kernel: ee.Kernel.circle(1)});

var combine_2_images = function(img1, img2) {
img1 = ee.Image(altering_IC_algorithms.adding_ndvi_evi(img1));
img2 = ee.Image(altering_IC_algorithms.adding_ndvi_evi(img2));
var combined_image = img1.addBands(img2);
return combined_image;
};

// Loads 3 or 4 pictures per year and combines them to one picture.
var img_classify_step2 = ee.Algorithms.If(
ee.Algorithms.IsEqual(year, '2016'),
img_classify_step2 = combine_2_images(
ee.Image('COPERNICUS/S2/20160505T103032_20160505T103027_T32UNU'),
ee.Image('COPERNICUS/S2/20160823T103022_20160823T103332_T32UNU')),
img_classify_step2 = ee.Algorithms.If(
ee.Algorithms.IsEqual(year, '2017'),
img_classify_step2 = combine_2_images(
ee.Image('COPERNICUS/S2_SR/20170517T102031_20170517T102352_T32UNU'),
ee.Image('COPERNICUS/S2_SR/20170626T102021_20170626T102321_T32UNU')),
img_classify_step2 = ee.Algorithms.If(
ee.Algorithms.IsEqual(year, '2018'),
img_classify_step2 = combine_2_images(
ee.Image('COPERNICUS/S2_SR/20180427T102019_20180427T102022_T32UNU'),
ee.Image('COPERNICUS/S2_SR/20180701T102021_20180701T102404_T32UNU')),
img_classify_step2 = ee.Algorithms.If(
ee.Algorithms.IsEqual(year, '2019'),
img_classify_step2 = combine_2_images(
ee.Image('COPERNICUS/S2_SR/20190517T102031_20190517T102508_T32UNU'),
ee.Image('COPERNICUS/S2_SR/20190629T103031_20190629T103537_T32UNU')),
img_classify_step2 = ee.Image([])
)
)
)
);
img_classify_step2 = ee.Image(img_classify_step2).updateMask(forest_mask).set('year', year);

// Classify deciduous and coniferous forest without explain.
var img_classified_dec_con = classifyImage(img_classify_step2, sample_data_classification_step_2, bands_step_2)
.set('classification_step:', 'deciduous_vs_coniferous', 'year:', year);
// Calculate classification probability for above classification step (step2).
var img_classified_dec_con_prob = classification_probability(img_classify_step2, sample_data_classification_step_2, bands_step_2)
.set('classification_step:', 'deciduous_vs_coniferous_probability', 'year:', year);

// Classify deciduous and coniferous forest with explain.
//var img_classified_dec_con = classifyImage(img_classify_step2, sample_data_classification_step_2, bands_step_2);

// Classify ash and beech trees within previously classified deciduous forest areas without explain.
var img_classify_step3 = img_classify_step2.updateMask(
img_classified_dec_con.remap([0, 1], [1, 0]));
var img_classified_ash_beech = classifyImage(img_classify_step3, sample_data_classification_step_3, bands_step_3)
.set('classification_step:', 'ash_vs_beech', 'year:', year);
// Calculate classificaction probability for above classification step (step_3).
var img_classified_ash_beech_prob = classification_probability(img_classify_step3, sample_data_classification_step_3, bands_step_3)
.set('classification_step:', 'ash_vs_beech_probability', 'year:', year);

// Classify ash and beech trees within previously classified deciduous forest areas with explain.
//var img_classified_dec_con_img = ee.Image(img_classified_dec_con.get('classified_image'));
//var img_classify_step3 = img_classify_step2.updateMask(
// img_classified_dec_con_img.remap([0, 1], [1 ,0]));
//var img_classified_ash_beech = classifyImage(img_classify_step3, sample_data_classification_step_3, bands_step_3);

// Combine classification results into dictionary for returning, without explain.
var classification_step_2_and_3 = ee.Dictionary({
'classification_step_2_classification': ee.Image(img_classified_dec_con),
'classification_step_2_probability': ee.Image(img_classified_dec_con_prob),
'classification_step_3_classification': ee.Image(img_classified_ash_beech),
'classification_step_3_probability': ee.Image(img_classified_ash_beech_prob)
});

// Combine classification results into dictionary for returning, with explain.
//var classification_step_2_step_3 = ee.Dictionary({
// 'classification_step_2': ee.Dictionary(img_classified_dec_con),
// 'classification_step_3': ee.Dictionary(img_classified_ash_beech)
//});


//return ee.Image(img_classified_ash_beech.get('classified_image'));
return classification_step_2_and_3;
};

exports.automatic_band_model_adjust = automatic_band_model_adjust;
exports.classifyImage = classifyImage;
exports.classification_probability = classification_probability;
exports.classifyForestMain = classifyForestMain;

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