ee.Algorithms.Image.Segmentation.SNIC

基于 SNIC（简单非迭代聚类）的超像素聚类。针对每个输入频段，输出一个聚类 ID 频段以及每个聚类的平均值。如果未将“种子”图片作为输入提供，则输出将包含一个“种子”波段，其中包含生成的种子位置。请参阅：Achanta, Radhakrishna 和 Susstrunk, Sabine，“Superpixels and Polygons using Simple Non-Iterative Clustering”，CVPR，2017 年。

用法返回
ee.Algorithms.Image.Segmentation.SNIC(image, size, compactness, connectivity, neighborhoodSize, seeds)图片
参数类型详细信息
image图片用于聚类的输入图片。
size整数，默认值：5超像素种子位置间距（以像素为单位）。如果提供了“种子”图片，则不会生成网格。
compactness浮点数，默认值：1紧凑性因子。值越大，聚类就越紧凑（方形）。将此值设置为 0 可停用空间距离加权。
connectivity整数，默认值：8连接。4 或 8。
neighborhoodSize整数，默认值：null平铺邻域大小（以避免平铺边界伪影）。默认值为 2 * size。
seeds图片，默认值：null如果提供，则任何非零值像素都将用作种子位置。接触（如“连通性”所指定）的像素被视为属于同一聚类。

示例

代码编辑器 (JavaScript)

// Note that the compactness and size parameters can have a significant impact
// on the result. They must be adjusted to meet image-specific characteristics
// and patterns, typically through trial. Pixel scale (map zoom level) is also
// important to consider. When exploring interactively through map tile
// visualization, the segmentation result it dependent on zoom level. If you
// need to evaluate the result at a specific scale, call .reproject() on the
// result, but do so with caution because it overrides the default scaling
// behavior that makes tile computation fast and efficient.


// Load a NAIP image for a neighborhood in Las Vegas.
var naip = ee.Image('USDA/NAIP/DOQQ/m_3611554_sw_11_1_20170613');

// Apply the SNIC algorithm to the image.
var snic = ee.Algorithms.Image.Segmentation.SNIC({
  image: naip,
  size: 30,
  compactness: 0.1,
  connectivity: 8,
});

// Display the original NAIP image as RGB.
// Lock map zoom to maintain the desired scale of the segmentation computation.
Map.setLocked(false, 18, 18);
Map.setCenter(-115.32053, 36.182016, 18);
Map.addLayer(naip, null, 'NAIP RGB');

// Display the clusters.
Map.addLayer(snic.randomVisualizer(), null, 'Clusters');

// Display the RGB cluster means.
var visParams = {
  bands: ['R_mean', 'G_mean', 'B_mean'],
  min: 0,
  max: 255
};
Map.addLayer(snic, visParams, 'RGB cluster means');

Python 设置

如需了解 Python API 和如何使用 geemap 进行交互式开发，请参阅 Python 环境页面。

import ee
import geemap.core as geemap

Colab (Python)

# Note that the compactness and size parameters can have a significant impact
# on the result. They must be adjusted to meet image-specific characteristics
# and patterns, typically through trial. Pixel scale (map zoom level) is also
# important to consider. When exploring interactively through map tile
# visualization, the segmentation result it dependent on zoom level. If you
# need to evaluate the result at a specific scale, call .reproject() on the
# result, but do so with caution because it overrides the default scaling
# behavior that makes tile computation fast and efficient.


# Load a NAIP image for a neighborhood in Las Vegas.
naip = ee.Image('USDA/NAIP/DOQQ/m_3611554_sw_11_1_20170613')

# Apply the SNIC algorithm to the image.
snic = ee.Algorithms.Image.Segmentation.SNIC(
    image=naip, size=30, compactness=0.1, connectivity=8
)

# Display the original NAIP image as RGB.
m = geemap.Map()
m.set_center(-115.32053, 36.182016, 18)
m.add_layer(naip, None, 'NAIP RGB')

# Display the clusters.
m.add_layer(snic.randomVisualizer(), None, 'Clusters')

# Display the RGB cluster means.
vis_params = {'bands': ['R_mean', 'G_mean', 'B_mean'], 'min': 0, 'max': 255}
m.add_layer(snic, vis_params, 'RGB cluster means')
m