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ee.Classifier.minimumDistance

지정된 거리 측정항목의 최소 거리 분류기를 만듭니다. CLASSIFICATION 모드에서는 가장 가까운 클래스가 반환됩니다. REGRESSION 모드에서는 가장 가까운 클래스 중심까지의 거리가 반환됩니다. RAW 모드에서는 모든 클래스 중심까지의 거리가 반환됩니다.

사용	반환 값
`ee.Classifier.minimumDistance(metric, kNearest)`	분류 기준

인수	유형	세부정보
`metric`	문자열, 기본값: 'euclidean'	사용할 거리 측정항목입니다. 옵션은 다음과 같습니다. 'euclidean': 정규화되지 않은 클래스 평균으로부터의 유클리드 거리입니다. 'cosine' - 정규화되지 않은 클래스 평균에서 스펙트럼 각도입니다. 'mahalanobis' - 클래스 평균에서의 마할라노비스 거리입니다. 'manhattan' - 정규화되지 않은 클래스 평균으로부터의 맨해튼 거리입니다.
`kNearest`	정수, 기본값: 1	1보다 크면 출력 모드 설정에 따라 가장 가까운 k개 이웃 또는 거리 배열이 결과에 포함됩니다. kNearest가 총 클래스 수보다 크면 클래스 수와 동일하게 설정됩니다.

예

코드 편집기 (JavaScript)

// A Sentinel-2 surface reflectance image, reflectance bands selected,
// serves as the source for training and prediction in this contrived example.
var img = ee.Image('COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG')
              .select('B.*');

// ESA WorldCover land cover map, used as label source in classifier training.
var lc = ee.Image('ESA/WorldCover/v100/2020');

// Remap the land cover class values to a 0-based sequential series.
var classValues = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100];
var remapValues = ee.List.sequence(0, 10);
var label = 'lc';
lc = lc.remap(classValues, remapValues).rename(label).toByte();

// Add land cover as a band of the reflectance image and sample 100 pixels at
// 10 m scale from each land cover class within a region of interest.
var roi = ee.Geometry.Rectangle(-122.347, 37.743, -122.024, 37.838);
var sample = img.addBands(lc).stratifiedSample({
  numPoints: 100,
  classBand: label,
  region: roi,
  scale: 10,
  geometries: true
});

// Add a random value field to the sample and use it to approximately split 80%
// of the features into a training set and 20% into a validation set.
sample = sample.randomColumn();
var trainingSample = sample.filter('random <= 0.8');
var validationSample = sample.filter('random > 0.8');

// Train a minimum distance classifier (Mahalanobis distance metric) from
// the training sample.
var trainedClassifier = ee.Classifier.minimumDistance('mahalanobis').train({
  features: trainingSample,
  classProperty: label,
  inputProperties: img.bandNames()
});

// Get information about the trained classifier.
print('Results of trained classifier', trainedClassifier.explain());

// Get a confusion matrix and overall accuracy for the training sample.
var trainAccuracy = trainedClassifier.confusionMatrix();
print('Training error matrix', trainAccuracy);
print('Training overall accuracy', trainAccuracy.accuracy());

// Get a confusion matrix and overall accuracy for the validation sample.
validationSample = validationSample.classify(trainedClassifier);
var validationAccuracy = validationSample.errorMatrix(label, 'classification');
print('Validation error matrix', validationAccuracy);
print('Validation accuracy', validationAccuracy.accuracy());

// Classify the reflectance image from the trained classifier.
var imgClassified = img.classify(trainedClassifier);

// Add the layers to the map.
var classVis = {
  min: 0,
  max: 10,
  palette: ['006400' ,'ffbb22', 'ffff4c', 'f096ff', 'fa0000', 'b4b4b4',
            'f0f0f0', '0064c8', '0096a0', '00cf75', 'fae6a0']
};
Map.setCenter(-122.184, 37.796, 12);
Map.addLayer(img, {bands: ['B11', 'B8', 'B3'], min: 100, max: 3500}, 'img');
Map.addLayer(lc, classVis, 'lc');
Map.addLayer(imgClassified, classVis, 'Classified');
Map.addLayer(roi, {color: 'white'}, 'ROI', false, 0.5);
Map.addLayer(trainingSample, {color: 'black'}, 'Training sample', false);
Map.addLayer(validationSample, {color: 'white'}, 'Validation sample', false);

Python 설정

Python API 및 대화형 개발을 위한 geemap 사용에 관한 자세한 내용은 Python 환경 페이지를 참고하세요.

import ee
import geemap.core as geemap

Colab (Python)

# A Sentinel-2 surface reflectance image, reflectance bands selected,
# serves as the source for training and prediction in this contrived example.
img = ee.Image(
    'COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG'
).select('B.*')

# ESA WorldCover land cover map, used as label source in classifier training.
lc = ee.Image('ESA/WorldCover/v100/2020')

# Remap the land cover class values to a 0-based sequential series.
class_values = [10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100]
remap_values = ee.List.sequence(0, 10)
label = 'lc'
lc = lc.remap(class_values, remap_values).rename(label).toByte()

# Add land cover as a band of the reflectance image and sample 100 pixels at
# 10 m scale from each land cover class within a region of interest.
roi = ee.Geometry.Rectangle(-122.347, 37.743, -122.024, 37.838)
sample = img.addBands(lc).stratifiedSample(
    numPoints=100, classBand=label, region=roi, scale=10, geometries=True
)

# Add a random value field to the sample and use it to approximately split 80%
# of the features into a training set and 20% into a validation set.
sample = sample.randomColumn()
training_sample = sample.filter('random <= 0.8')
validation_sample = sample.filter('random > 0.8')

# Train a minimum distance classifier (Mahalanobis distance metric) from
# the training sample.
trained_classifier = ee.Classifier.minimumDistance('mahalanobis').train(
    features=training_sample,
    classProperty=label,
    inputProperties=img.bandNames(),
)

# Get information about the trained classifier.
display('Results of trained classifier', trained_classifier.explain())

# Get a confusion matrix and overall accuracy for the training sample.
train_accuracy = trained_classifier.confusionMatrix()
display('Training error matrix', train_accuracy)
display('Training overall accuracy', train_accuracy.accuracy())

# Get a confusion matrix and overall accuracy for the validation sample.
validation_sample = validation_sample.classify(trained_classifier)
validation_accuracy = validation_sample.errorMatrix(label, 'classification')
display('Validation error matrix', validation_accuracy)
display('Validation accuracy', validation_accuracy.accuracy())

# Classify the reflectance image from the trained classifier.
img_classified = img.classify(trained_classifier)

# Add the layers to the map.
class_vis = {
    'min': 0,
    'max': 10,
    'palette': [
        '006400',
        'ffbb22',
        'ffff4c',
        'f096ff',
        'fa0000',
        'b4b4b4',
        'f0f0f0',
        '0064c8',
        '0096a0',
        '00cf75',
        'fae6a0',
    ],
}
m = geemap.Map()
m.set_center(-122.184, 37.796, 12)
m.add_layer(
    img, {'bands': ['B11', 'B8', 'B3'], 'min': 100, 'max': 3500}, 'img'
)
m.add_layer(lc, class_vis, 'lc')
m.add_layer(img_classified, class_vis, 'Classified')
m.add_layer(roi, {'color': 'white'}, 'ROI', False, 0.5)
m.add_layer(training_sample, {'color': 'black'}, 'Training sample', False)
m.add_layer(
    validation_sample, {'color': 'white'}, 'Validation sample', False
)
m

ee.Classifier.minimumDistance 컬렉션을 사용해 정리하기 내 환경설정을 기준으로 콘텐츠를 저장하고 분류하세요.

예

코드 편집기 (JavaScript)

Colab (Python)

ee.Classifier.minimumDistance