gee python:利用核函数对影像进行平滑处理和边缘提取分析

安装地球引擎API和geemap 安装地球引擎的Python API和geemap。geemap Python包是建立在ipyleaflet和folium包之上的，它实现了几个与地球引擎数据层交互的方法，比如Map.addLayer()、Map.setCenter()和Map.centerObject()。下面的脚本检查geemap包是否已经安装。如果没有，它将安装geemap，它会自动安装其依赖项，包括earthengine-api、folium和ipyleaflet。

代码语言：javascript复制

# Installs geemap package
import subprocess

try:
    import geemap
except ImportError:
    print('Installing geemap ...')
    subprocess.check_call(["python", '-m', 'pip', 'install', 'geemap'])

代码语言：javascript复制

import ee
import geemap

使用的函数： **ee.Kernel.square(*args, kwargs) Generates a square-shaped boolean kernel.这里使用这个核函数创建一个布尔类型的内核。参数类型的设定中我们可以使用半径和单位进行设定。 Args: radius: The radius of the kernel to generate. units: The system of measurement for the kernel (‘pixels’ or ‘meters’). If the kernel is specified in meters, it will resize when the zoom-level is changed. normalize: Normalize the kernel values to sum to 1. magnitude: Scale each value by this amount.

**image.convolve(*args, kwargs) Convolves each band of an image with the given kernel. 将每一个指定的影像波段转化为给定的核函数，通过与 Boxcar 内核卷积来平滑图像。

Args: image: The image to convolve. kernel: The kernel to convolve with.

**ee.Kernel.laplacian8(*args, kwargs) Generates a 3x3 Laplacian-8 edge-detection kernel.

Args: magnitude: Scale each value by this amount. normalize: Normalize the kernel values to sum to 1

生成 3x3 Laplacian-8 边缘检测内核。参数：大小：按此量缩放每个值。标准化：将核值标准化为总和为 1

代码语言：javascript复制

# Add Earth Engine dataset
# Load and display an image.
image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318')
Map.setCenter(-121.9785, 37.8694, 11)
Map.addLayer(image, {'bands': ['B5', 'B4', 'B3'], 'max': 0.5}, 'input image')

# Define a boxcar or low-pass kernel.
# boxcar = ee.Kernel.square({
#   'radius': 7, 'units': 'pixels', 'normalize': True
# })

boxcar = ee.Kernel.square(7, 'pixels', True)

# Smooth the image by convolving with the boxcar kernel.
smooth = image.convolve(boxcar)
Map.addLayer(smooth, {'bands': ['B5', 'B4', 'B3'], 'max': 0.5}, 'smoothed')

# Define a Laplacian, or edge-detection kernel.
laplacian = ee.Kernel.laplacian8(1, False)

# Apply the edge-detection kernel.
edgy = image.convolve(laplacian)
Map.addLayer(edgy,
             {'bands': ['B5', 'B4', 'B3'], 'max': 0.5},
             'edges')

python kernel image 函数内核

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