ee.Image.distance

지정된 거리 커널을 사용하여 각 밴드에서 0이 아닌 가장 가까운 픽셀까지의 거리를 계산합니다.

사용반환 값
Image.distance(kernel, skipMasked)이미지
인수유형세부정보
다음과 같은 경우: image이미지입력 이미지입니다.
kernel커널, 기본값: null거리 커널입니다. chebyshev, euclidean, manhattan 중 하나입니다.
skipMasked불리언, 기본값: true해당 입력 픽셀이 마스크 처리된 경우 출력 픽셀을 마스크 처리합니다.

예

코드 편집기 (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');

Python 설정

Python API 및 geemap를 사용한 대화형 개발에 관한 자세한 내용은 Python 환경 페이지를 참고하세요.

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