ee.Image.distance

Calcule la distance au pixel non nul le plus proche dans chaque bande, à l'aide du noyau de distance spécifié.

Utilisation	Renvoie
Image.distance(kernel, skipMasked)	Image

Argument	Type	Détails
ceci : image	Image	Image d'entrée.
kernel	Kernel, valeur par défaut : null	Noyau de distance. L'une des valeurs suivantes : "chebyshev", "euclidean" ou "manhattan".
skipMasked	Booléen, valeur par défaut : true	Masque les pixels de sortie si le pixel d'entrée correspondant est masqué.

Exemples

Éditeur de code (JavaScript)

// The objective is to determine the per-pixel distance to a target
// feature (pixel value). In this example, the target feature is water in a
// land cover map.

// Import a Dynamic World land cover image and subset the 'label' band.
var lcImg = ee.Image(
  'GOOGLE/DYNAMICWORLD/V1/20210726T171859_20210726T172345_T14TQS')
  .select('label');

// Create a binary image where the target feature is value 1, all else 0.
// In the Dynamic World map, water is represented as value 0, so we use the
// ee.Image.eq() relational operator to set it to 1.
var targetImg = lcImg.eq(0);

// Set a max distance from target pixels to consider in the analysis. Pixels
// with distance greater than this value from target pixels will be masked out.
// Here, we are using units of meters, but the distance kernels also accept
// units of pixels.
var maxDistM = 10000;  // 10 km

// Calculate distance to target pixels. Several distance kernels are provided.

// Euclidean distance.
var euclideanKernel = ee.Kernel.euclidean(maxDistM, 'meters');
var euclideanDist = targetImg.distance(euclideanKernel);
var vis = {min: 0, max: maxDistM};
Map.setCenter(-95.68, 46.46, 9);
Map.addLayer(euclideanDist, vis, 'Euclidean distance to target pixels');

// Manhattan distance.
var manhattanKernel = ee.Kernel.manhattan(maxDistM, 'meters');
var manhattanDist = targetImg.distance(manhattanKernel);
Map.addLayer(manhattanDist, vis, 'Manhattan distance to target pixels', false);

// Chebyshev distance.
var chebyshevKernel = ee.Kernel.chebyshev(maxDistM, 'meters');
var chebyshevDist = targetImg.distance(chebyshevKernel);
Map.addLayer(chebyshevDist, vis, 'Chebyshev distance to target pixels', false);

// Add the target layer to the map; water is blue, all else masked out.
Map.addLayer(targetImg.mask(targetImg), {palette: 'blue'}, 'Target pixels');

Configuration de Python

Consultez la page Environnement Python pour en savoir plus sur l'API Python et sur l'utilisation de geemap pour le développement interactif.

import ee
import geemap.core as geemap

Colab (Python)

# The objective is to determine the per-pixel distance to a target
# feature (pixel value). In this example, the target feature is water in a
# land cover map.

# Import a Dynamic World land cover image and subset the 'label' band.
lc_img = ee.Image(
    'GOOGLE/DYNAMICWORLD/V1/20210726T171859_20210726T172345_T14TQS'
).select('label')

# Create a binary image where the target feature is value 1, all else 0.
# In the Dynamic World map, water is represented as value 0, so we use the
# ee.Image.eq() relational operator to set it to 1.
target_img = lc_img.eq(0)

# Set a max distance from target pixels to consider in the analysis. Pixels
# with distance greater than this value from target pixels will be masked out.
# Here, we are using units of meters, but the distance kernels also accept
# units of pixels.
max_dist_m = 10000  # 10 km

# Calculate distance to target pixels. Several distance kernels are provided.

# Euclidean distance.
euclidean_kernel = ee.Kernel.euclidean(max_dist_m, 'meters')
euclidean_dist = target_img.distance(euclidean_kernel)
vis = {'min': 0, 'max': max_dist_m}
m = geemap.Map()
m.set_center(-95.68, 46.46, 9)
m.add_layer(euclidean_dist, vis, 'Euclidean distance to target pixels')

# Manhattan distance.
manhattan_kernel = ee.Kernel.manhattan(max_dist_m, 'meters')
manhattan_dist = target_img.distance(manhattan_kernel)
m.add_layer(
    manhattan_dist, vis, 'Manhattan distance to target pixels', False
)

# Chebyshev distance.
chebyshev_kernel = ee.Kernel.chebyshev(max_dist_m, 'meters')
chebyshev_dist = target_img.distance(chebyshev_kernel)
m.add_layer(
    chebyshev_dist, vis, 'Chebyshev distance to target pixels', False
)

# Add the target layer to the map water is blue, all else masked out.
m.add_layer(
    target_img.mask(target_img), {'palette': 'blue'}, 'Target pixels'
)
m