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ee.Image.sample
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
对图片的像素进行采样,并以 FeatureCollection 形式返回。每个特征在输入图片中每个波段都将有 1 个属性。请注意,默认行为是舍弃与遮盖像素相交的特征,这会导致属性值为 null(请参阅 dropNulls 实参)。
用法 | 返回 |
---|
Image.sample(region, scale, projection, factor, numPixels, seed, dropNulls, tileScale, geometries) | FeatureCollection |
参数 | 类型 | 详细信息 |
---|
此:image | 图片 | 要抽样的图片。 |
region | 几何图形,默认值:null | 要从中抽样的区域。如果未指定,则使用映像的整个占用空间。 |
scale | 浮点数,默认值:null | 要采样的投影的标称比例(以米为单位)。 |
projection | 投影,默认值:null | 要进行抽样的投影。如果未指定,则使用映像第一个波段的投影。如果除了缩放比例之外还指定了此参数,则会重新缩放到指定的缩放比例。 |
factor | 浮点数,默认值:null | 一个介于 (0, 1] 范围内的子采样率。如果指定了此参数,则不得指定“numPixels”。默认值为不进行子采样。 |
numPixels | Long,默认值:null | 要采样的像素的大致数量。如果指定了此字段,则不得指定“factor”。 |
seed | 整数,默认值:0 | 用于子采样的随机化种子。 |
dropNulls | 布尔值,默认值:true | 对结果进行后过滤,以舍弃具有 null 值属性的特征。 |
tileScale | 浮点数,默认值:1 | 用于减小聚合图块大小的缩放比例;使用较大的 tileScale(例如,2 或 4)可能会启用内存不足的计算(使用默认值)。 |
geometries | 布尔值,默认值:false | 如果为 true,则将采样像素的中心添加为输出要素的几何属性。否则,系统会省略几何图形(节省内存)。 |
示例
代码编辑器 (JavaScript)
// Demonstrate extracting pixels from an image as features with
// ee.Image.sample(), and show how the features are aligned with the pixels.
// An image with one band of elevation data.
var image = ee.Image('CGIAR/SRTM90_V4');
var VIS_MIN = 1620;
var VIS_MAX = 1650;
Map.addLayer(image, {min: VIS_MIN, max: VIS_MAX}, 'SRTM');
// Region to sample.
var region = ee.Geometry.Polygon(
[[[-110.006, 40.002],
[-110.006, 39.999],
[-109.995, 39.999],
[-109.995, 40.002]]], null, false);
// Show region on the map.
Map.setCenter(-110, 40, 16);
Map.addLayer(ee.FeatureCollection([region]).style({"color": "00FF0022"}));
// Perform sampling; convert image pixels to features.
var samples = image.sample({
region: region,
// Default (false) is no geometries in the output.
// When set to true, each feature has a Point geometry at the center of the
// image pixel.
geometries: true,
// The scale is not specified, so the resolution of the image will be used,
// and there is a feature for every pixel. If we give a scale parameter, the
// image will be resampled and there will be more or fewer features.
//
// scale: 200,
});
// Visualize sample data using ee.FeatureCollection.style().
var styled = samples
.map(function (feature) {
return feature.set('style', {
pointSize: feature.getNumber('elevation').unitScale(VIS_MIN, VIS_MAX)
.multiply(15),
});
})
.style({
color: '000000FF',
fillColor: '00000000',
styleProperty: 'style',
neighborhood: 6, // increase to correctly draw large points
});
Map.addLayer(styled);
// Each sample feature has a point geometry and a property named 'elevation'
// corresponding to the band named 'elevation' of the image. If there are
// multiple bands they will become multiple properties. This will print:
//
// geometry: Point (-110.01, 40.00)
// properties:
// elevation: 1639
print(samples.first());
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)
# Demonstrate extracting pixels from an image as features with
# ee.Image.sample(), and show how the features are aligned with the pixels.
# An image with one band of elevation data.
image = ee.Image('CGIAR/SRTM90_V4')
vis_min = 1620
vis_max = 1650
m = geemap.Map()
m.add_layer(image, {'min': vis_min, 'max': vis_max}, 'SRTM')
# Region to sample.
region = ee.Geometry.Polygon(
[[
[-110.006, 40.002],
[-110.006, 39.999],
[-109.995, 39.999],
[-109.995, 40.002],
]],
None,
False,
)
# Show region on the map.
m.set_center(-110, 40, 16)
m.add_layer(ee.FeatureCollection([region]).style(color='00FF0022'))
# Perform sampling convert image pixels to features.
samples = image.sample(
region=region,
# Default (False) is no geometries in the output.
# When set to True, each feature has a Point geometry at the center of the
# image pixel.
geometries=True,
# The scale is not specified, so the resolution of the image will be used,
# and there is a feature for every pixel. If we give a scale parameter, the
# image will be resampled and there will be more or fewer features.
#
# scale=200,
)
def scale_point_size(feature):
elevation = feature.getNumber('elevation')
point_size = elevation.unitScale(vis_min, vis_max).multiply(15)
feature.set('style', {'pointSize': point_size})
return feature
# Visualize sample data using ee.FeatureCollection.style().
styled = samples.map(scale_point_size).style(
color='000000FF',
fillColor='00000000',
styleProperty='style',
neighborhood=6, # increase to correctly draw large points
)
m.add_layer(styled)
display(m)
# Each sample feature has a point geometry and a property named 'elevation'
# corresponding to the band named 'elevation' of the image. If there are
# multiple bands they will become multiple properties. This will print:
#
# geometry: Point (-110.01, 40.00)
# properties:
# elevation: 1639
display(samples.first())
如未另行说明,那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可,并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情,请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC):2025-07-26。
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