Komposisi dan Mosaik

Secara umum, komposisi mengacu pada proses penggabungan gambar yang tumpang-tindih secara spasial menjadi satu gambar berdasarkan fungsi agregasi. Mosaik mengacu pada proses penyusunan set data gambar secara spasial untuk menghasilkan gambar yang kontinu secara spasial. Di Earth Engine, istilah ini digunakan secara bergantian, meskipun komposisi dan pembuatan mosaik didukung. Misalnya, pertimbangkan tugas untuk mengomposisi beberapa gambar di lokasi yang sama. Misalnya, menggunakan satu National Agriculture Imagery Program (NAIP) Digital Orthophoto Quarter Quadrangle (DOQQ) pada waktu yang berbeda, contoh berikut menunjukkan pembuatan gabungan nilai maksimum:

Editor Kode (JavaScript)

// Load three NAIP quarter quads in the same location, different times.
var naip2004_2012 = ee.ImageCollection('USDA/NAIP/DOQQ')
  .filterBounds(ee.Geometry.Point(-71.08841, 42.39823))
  .filterDate('2004-07-01', '2012-12-31')
  .select(['R', 'G', 'B']);

// Temporally composite the images with a maximum value function.
var composite = naip2004_2012.max();
Map.setCenter(-71.12532, 42.3712, 12);
Map.addLayer(composite, {}, 'max value composite');

Penyiapan Python

Lihat halaman Lingkungan Python untuk mengetahui informasi tentang Python API dan penggunaan geemap untuk pengembangan interaktif.

import ee
import geemap.core as geemap

Colab (Python)

# Load three NAIP quarter quads in the same location, different times.
naip_2004_2012 = (
    ee.ImageCollection('USDA/NAIP/DOQQ')
    .filterBounds(ee.Geometry.Point(-71.08841, 42.39823))
    .filterDate('2004-07-01', '2012-12-31')
    .select(['R', 'G', 'B'])
)

# Temporally composite the images with a maximum value function.
composite = naip_2004_2012.max()
m.set_center(-71.12532, 42.3712, 12)
m.add_layer(composite, {}, 'max value composite')
m

Pertimbangkan kebutuhan untuk membuat mosaik empat DOQQ yang berbeda secara bersamaan, tetapi lokasinya berbeda. Contoh berikut menunjukkan bahwa menggunakan imageCollection.mosaic():

Editor Kode (JavaScript)

// Load four 2012 NAIP quarter quads, different locations.
var naip2012 = ee.ImageCollection('USDA/NAIP/DOQQ')
  .filterBounds(ee.Geometry.Rectangle(-71.17965, 42.35125, -71.08824, 42.40584))
  .filterDate('2012-01-01', '2012-12-31');

// Spatially mosaic the images in the collection and display.
var mosaic = naip2012.mosaic();
Map.setCenter(-71.12532, 42.3712, 12);
Map.addLayer(mosaic, {}, 'spatial mosaic');

Penyiapan Python

Lihat halaman Lingkungan Python untuk mengetahui informasi tentang Python API dan penggunaan geemap untuk pengembangan interaktif.

import ee
import geemap.core as geemap

Colab (Python)

# Load four 2012 NAIP quarter quads, different locations.
naip_2012 = (
    ee.ImageCollection('USDA/NAIP/DOQQ')
    .filterBounds(
        ee.Geometry.Rectangle(-71.17965, 42.35125, -71.08824, 42.40584)
    )
    .filterDate('2012-01-01', '2012-12-31')
)

# Spatially mosaic the images in the collection and display.
mosaic = naip_2012.mosaic()
m = geemap.Map()
m.set_center(-71.12532, 42.3712, 12)
m.add_layer(mosaic, {}, 'spatial mosaic')

Perhatikan bahwa ada beberapa tumpang-tindih dalam DOQQ pada contoh sebelumnya. Metode mosaic() menggabungkan gambar yang tumpang-tindih sesuai dengan urutannya dalam koleksi (terakhir di atas). Untuk mengontrol sumber piksel dalam mosaik (atau komposit), gunakan mask gambar. Misalnya, kode berikut menggunakan nilai minimum pada indeks spektral untuk menyamarkan data gambar dalam mosaik:

Editor Kode (JavaScript)

// Load a NAIP quarter quad, display.
var naip = ee.Image('USDA/NAIP/DOQQ/m_4207148_nw_19_1_20120710');
Map.setCenter(-71.0915, 42.3443, 14);
Map.addLayer(naip, {}, 'NAIP DOQQ');

// Create the NDVI and NDWI spectral indices.
var ndvi = naip.normalizedDifference(['N', 'R']);
var ndwi = naip.normalizedDifference(['G', 'N']);

// Create some binary images from thresholds on the indices.
// This threshold is designed to detect bare land.
var bare1 = ndvi.lt(0.2).and(ndwi.lt(0.3));
// This detects bare land with lower sensitivity. It also detects shadows.
var bare2 = ndvi.lt(0.2).and(ndwi.lt(0.8));

// Define visualization parameters for the spectral indices.
var ndviViz = {min: -1, max: 1, palette: ['FF0000', '00FF00']};
var ndwiViz = {min: 0.5, max: 1, palette: ['00FFFF', '0000FF']};

// Mask and mosaic visualization images.  The last layer is on top.
var mosaic = ee.ImageCollection([
  // NDWI > 0.5 is water.  Visualize it with a blue palette.
  ndwi.updateMask(ndwi.gte(0.5)).visualize(ndwiViz),
  // NDVI > 0.2 is vegetation.  Visualize it with a green palette.
  ndvi.updateMask(ndvi.gte(0.2)).visualize(ndviViz),
  // Visualize bare areas with shadow (bare2 but not bare1) as gray.
  bare2.updateMask(bare2.and(bare1.not())).visualize({palette: ['AAAAAA']}),
  // Visualize the other bare areas as white.
  bare1.updateMask(bare1).visualize({palette: ['FFFFFF']}),
]).mosaic();
Map.addLayer(mosaic, {}, 'Visualization mosaic');

Penyiapan Python

Lihat halaman Lingkungan Python untuk mengetahui informasi tentang Python API dan penggunaan geemap untuk pengembangan interaktif.

import ee
import geemap.core as geemap

Colab (Python)

# Load a NAIP quarter quad, display.
naip = ee.Image('USDA/NAIP/DOQQ/m_4207148_nw_19_1_20120710')
m = geemap.Map()
m.set_center(-71.0915, 42.3443, 14)
m.add_layer(naip, {}, 'NAIP DOQQ')

# Create the NDVI and NDWI spectral indices.
ndvi = naip.normalizedDifference(['N', 'R'])
ndwi = naip.normalizedDifference(['G', 'N'])

# Create some binary images from thresholds on the indices.
# This threshold is designed to detect bare land.
bare_1 = ndvi.lt(0.2).And(ndwi.lt(0.3))
# This detects bare land with lower sensitivity. It also detects shadows.
bare_2 = ndvi.lt(0.2).And(ndwi.lt(0.8))

# Mask and mosaic visualization images. The last layer is on top.
mosaic = ee.ImageCollection([
    # NDWI > 0.5 is water. Visualize it with a blue palette.
    ndwi.updateMask(ndwi.gte(0.5)).visualize(
        min=0.5, max=1, palette=['00FFFF', '0000FF']
    ),
    # NDVI > 0.2 is vegetation. Visualize it with a green palette.
    ndvi.updateMask(ndvi.gte(0.2)).visualize(
        min=-1, max=1, palette=['FF0000', '00FF00']
    ),
    # Visualize bare areas with shadow (bare_2 but not bare_1) as gray.
    bare_2.updateMask(bare_2.And(bare_1.Not())).visualize(palette=['AAAAAA']),
    # Visualize the other bare areas as white.
    bare_1.updateMask(bare_1).visualize(palette=['FFFFFF']),
]).mosaic()
m.add_layer(mosaic, {}, 'Visualization mosaic')
m

Untuk membuat komposit yang memaksimalkan band arbitrer dalam input, gunakan imageCollection.qualityMosaic(). Metode qualityMosaic() menetapkan setiap piksel dalam komposit berdasarkan gambar dalam koleksi yang memiliki nilai maksimum untuk band yang ditentukan. Misalnya, kode berikut menunjukkan pembuatan komposit piksel paling hijau dan komposit nilai terbaru:

Editor Kode (JavaScript)

// Define a function that scales and masks Landsat 8 surface reflectance images.
function prepSrL8(image) {
  // Develop masks for unwanted pixels (fill, cloud, cloud shadow).
  var qaMask = image.select('QA_PIXEL').bitwiseAnd(parseInt('11111', 2)).eq(0);
  var saturationMask = image.select('QA_RADSAT').eq(0);

  // Apply the scaling factors to the appropriate bands.
  var getFactorImg = function(factorNames) {
    var factorList = image.toDictionary().select(factorNames).values();
    return ee.Image.constant(factorList);
  };
  var scaleImg = getFactorImg([
    'REFLECTANCE_MULT_BAND_.|TEMPERATURE_MULT_BAND_ST_B10']);
  var offsetImg = getFactorImg([
    'REFLECTANCE_ADD_BAND_.|TEMPERATURE_ADD_BAND_ST_B10']);
  var scaled = image.select('SR_B.|ST_B10').multiply(scaleImg).add(offsetImg);

  // Replace original bands with scaled bands and apply masks.
  return image.addBands(scaled, null, true)
    .updateMask(qaMask).updateMask(saturationMask);
}

// This function masks clouds and adds quality bands to Landsat 8 images.
var addQualityBands = function(image) {
  // Normalized difference vegetation index.
  var ndvi = image.normalizedDifference(['SR_B5', 'SR_B4']);
  // Image timestamp as milliseconds since Unix epoch.
  var millis = ee.Image(image.getNumber('system:time_start'))
                   .rename('millis').toFloat();
  return prepSrL8(image).addBands([ndvi, millis]);
};

// Load a 2014 Landsat 8 ImageCollection.
// Map the cloud masking and quality band function over the collection.
var collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
  .filterDate('2014-06-01', '2014-12-31')
  .map(addQualityBands);

// Create a cloud-free, most recent value composite.
var recentValueComposite = collection.qualityMosaic('millis');

// Create a greenest pixel composite.
var greenestPixelComposite = collection.qualityMosaic('nd');

// Display the results.
Map.setCenter(-122.374, 37.8239, 12); // San Francisco Bay
var vizParams = {bands: ['SR_B5', 'SR_B4', 'SR_B3'], min: 0, max: 0.4};
Map.addLayer(recentValueComposite, vizParams, 'Recent value composite');
Map.addLayer(greenestPixelComposite, vizParams, 'Greenest pixel composite');

// Compare to a cloudy image in the collection.
var cloudy = ee.Image('LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140825');
Map.addLayer(cloudy, {bands: ['B5', 'B4', 'B3'], min: 0, max: 0.4}, 'Cloudy');

Penyiapan Python

Lihat halaman Lingkungan Python untuk mengetahui informasi tentang Python API dan penggunaan geemap untuk pengembangan interaktif.

import ee
import geemap.core as geemap

Colab (Python)

# Define a function that scales and masks Landsat 8 surface reflectance images.
def prep_sr_l8(image):
  # Develop masks for unwanted pixels (fill, cloud, cloud shadow).
  qa_mask = image.select('QA_PIXEL').bitwiseAnd(int('11111', 2)).eq(0)
  saturation_mask = image.select('QA_RADSAT').eq(0)

  # Helper function to create image from scaling factors.
  def get_factor_img(factor_names):
    factor_list = image.toDictionary().select(factor_names).values()
    return ee.Image.constant(factor_list)

  # Apply the scaling factors to the appropriate bands.
  scale_img = get_factor_img(
      ['REFLECTANCE_MULT_BAND_.|TEMPERATURE_MULT_BAND_ST_B10']
  )
  offset_img = get_factor_img(
      ['REFLECTANCE_ADD_BAND_.|TEMPERATURE_ADD_BAND_ST_B10']
  )
  scaled = image.select('SR_B.|ST_B10').multiply(scale_img).add(offset_img)

  # Replace original bands with scaled bands and apply masks.
  return (
      image.addBands(scaled, None, True)
      .updateMask(qa_mask)
      .updateMask(saturation_mask)
  )


# This function masks clouds and adds quality bands to Landsat 8 images.
def add_quality_bands(image):
  # Normalized difference vegetation index.
  ndvi = image.normalizedDifference(['SR_B5', 'SR_B4'])
  # Image timestamp as milliseconds since Unix epoch.
  millis = (
      ee.Image(image.getNumber('system:time_start')).rename('millis').toFloat()
  )
  return prep_sr_l8(image).addBands([ndvi, millis])


# Load a 2014 Landsat 8 ImageCollection.
# Map the cloud masking and quality band function over the collection.
collection = (
    ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
    .filterDate('2014-06-01', '2014-12-31')
    .map(add_quality_bands)
)

# Create a cloud-free, most recent value composite.
recent_value_composite = collection.qualityMosaic('millis')

# Create a greenest pixel composite.
greenest_pixel_composite = collection.qualityMosaic('nd')

# Display the results.
m = geemap.Map()
m.set_center(-122.374, 37.8239, 12)  # San Francisco Bay
viz_params = {'bands': ['SR_B5', 'SR_B4', 'SR_B3'], 'min': 0, 'max': 0.4}
m.add_layer(recent_value_composite, viz_params, 'Recent value composite')
m.add_layer(greenest_pixel_composite, viz_params, 'Greenest pixel composite')

# Compare to a cloudy image in the collection.
cloudy = ee.Image('LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140825')
m.add_layer(
    cloudy, {'bands': ['B5', 'B4', 'B3'], 'min': 0, 'max': 0.4}, 'Cloudy'
)
m

Gunakan alat pemeriksa untuk memeriksa nilai piksel di berbagai lokasi dalam komposit. Perhatikan bahwa band millis (stempel waktu) bervariasi menurut lokasi, yang menunjukkan bahwa piksel yang berbeda berasal dari waktu yang berbeda.