F32b Checkpoint

Import libraries¶

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import ee
import geemap

import ee import geemap

Create an interactive map¶

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Map = geemap.Map(center=[40, -100], zoom=4)

Map = geemap.Map(center=[40, -100], zoom=4)

Add Earth Engine Python script¶

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# Add Earth Engine dataset
image = ee.Image("USGS/SRTMGL1_003")

#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#  Chapter:      F3.2 Neighborhood-Based Image Transformation
#  Checkpoint:   F32b
#  Authors:      Karen, Andrea, David, Nick
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Create and print a uniform kernel to see its weights.
print("A uniform kernel:", ee.Kernel.square(2))

# Define a point of interest in Odessa, Washington, USA.
point = ee.Geometry.Point([-118.71845096212049, 47.15743083101999])
Map.centerObject(point)

# Load NAIP data.
imageNAIP = (
    ee.ImageCollection("USDA/NAIP/DOQQ")
    .filterBounds(point)
    .filter(ee.Filter.date("2017-01-01", "2018-12-31"))
    .first()
)

Map.centerObject(point, 17)

TrueColor = {"bands": ["R", "G", "B"], "min": 0, "max": 255}
Map.addLayer(imageNAIP, TrueColor, "True color")

# Begin smoothing example.
# Define a square, uniform kernel.
uniformKernel = ee.Kernel.square(
    {
        "radius": 2,
        "units": "meters",
    }
)

# Convolve the image by convolving with the smoothing kernel.
smoothed = imageNAIP.convolve(uniformKernel)
Map.addLayer(smoothed, {"min": 0, "max": 255}, "smoothed image")

# Begin Gaussian smoothing example.
# Print a Gaussian kernel to see its weights.
print("A Gaussian kernel:", ee.Kernel.gaussian(2))

# Define a square Gaussian kernel:
gaussianKernel = ee.Kernel.gaussian(
    {
        "radius": 2,
        "units": "meters",
    }
)

# Convolve the image with the Gaussian kernel.
gaussian = imageNAIP.convolve(gaussianKernel)
Map.addLayer(gaussian, {"min": 0, "max": 255}, "Gaussian smoothed image")

# Begin edge detection example.
# For edge detection, define a Laplacian kernel.
laplacianKernel = ee.Kernel.laplacian8()

# Print the kernel to see its weights.
print("Edge detection Laplacian kernel:", laplacianKernel)

# Convolve the image with the Laplacian kernel.
edges = imageNAIP.convolve(laplacianKernel)
Map.addLayer(edges, {"min": 0, "max": 255}, "Laplacian convolution image")

# Begin image sharpening example.
# Define a "fat" Gaussian kernel.
fat = ee.Kernel.gaussian({"radius": 3, "sigma": 3, "magnitude": -1, "units": "meters"})

# Define a "skinny" Gaussian kernel.
skinny = ee.Kernel.gaussian({"radius": 3, "sigma": 0.5, "units": "meters"})

# Compute a difference-of-Gaussians (DOG) kernel.
dog = fat.add(skinny)

# Print the kernel to see its weights.
print("DoG kernel for image sharpening", dog)

# Add the DoG convolved image to the original image.
sharpened = imageNAIP.add(imageNAIP.convolve(dog))
Map.addLayer(sharpened, {"min": 0, "max": 255}, "DoG edge enhancement")

#  -----------------------------------------------------------------------
#  CHECKPOINT
#  -----------------------------------------------------------------------

# Begin median example.
# Pass a median neighborhood filter using our uniformKernel.
median = imageNAIP.reduceNeighborhood(
    {"reducer": ee.Reducer.median(), "kernel": uniformKernel}
)

Map.addLayer(median, {"min": 0, "max": 255}, "Median Neighborhood Filter")

# Mode example
# Create and display a simple two-class image.
veg = imageNAIP.select("N").gt(200)

# Display the two-class (binary) result.
binaryVis = {"min": 0, "max": 1, "palette": ["black", "green"]}
Map.addLayer(veg, binaryVis, "Vegetation categorical image")

# Compute the mode in each 5x5 neighborhood and display the result.
mode = veg.reduceNeighborhood({"reducer": ee.Reducer.mode(), "kernel": uniformKernel})

Map.addLayer(
    mode, binaryVis, "Mode Neighborhood Filter on Vegetation categorical image"
)

#  -----------------------------------------------------------------------
#  CHECKPOINT
#  -----------------------------------------------------------------------

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: F3.2 Neighborhood-Based Image Transformation # Checkpoint: F32b # Authors: Karen, Andrea, David, Nick # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Create and print a uniform kernel to see its weights. print("A uniform kernel:", ee.Kernel.square(2)) # Define a point of interest in Odessa, Washington, USA. point = ee.Geometry.Point([-118.71845096212049, 47.15743083101999]) Map.centerObject(point) # Load NAIP data. imageNAIP = ( ee.ImageCollection("USDA/NAIP/DOQQ") .filterBounds(point) .filter(ee.Filter.date("2017-01-01", "2018-12-31")) .first() ) Map.centerObject(point, 17) TrueColor = {"bands": ["R", "G", "B"], "min": 0, "max": 255} Map.addLayer(imageNAIP, TrueColor, "True color") # Begin smoothing example. # Define a square, uniform kernel. uniformKernel = ee.Kernel.square( { "radius": 2, "units": "meters", } ) # Convolve the image by convolving with the smoothing kernel. smoothed = imageNAIP.convolve(uniformKernel) Map.addLayer(smoothed, {"min": 0, "max": 255}, "smoothed image") # Begin Gaussian smoothing example. # Print a Gaussian kernel to see its weights. print("A Gaussian kernel:", ee.Kernel.gaussian(2)) # Define a square Gaussian kernel: gaussianKernel = ee.Kernel.gaussian( { "radius": 2, "units": "meters", } ) # Convolve the image with the Gaussian kernel. gaussian = imageNAIP.convolve(gaussianKernel) Map.addLayer(gaussian, {"min": 0, "max": 255}, "Gaussian smoothed image") # Begin edge detection example. # For edge detection, define a Laplacian kernel. laplacianKernel = ee.Kernel.laplacian8() # Print the kernel to see its weights. print("Edge detection Laplacian kernel:", laplacianKernel) # Convolve the image with the Laplacian kernel. edges = imageNAIP.convolve(laplacianKernel) Map.addLayer(edges, {"min": 0, "max": 255}, "Laplacian convolution image") # Begin image sharpening example. # Define a "fat" Gaussian kernel. fat = ee.Kernel.gaussian({"radius": 3, "sigma": 3, "magnitude": -1, "units": "meters"}) # Define a "skinny" Gaussian kernel. skinny = ee.Kernel.gaussian({"radius": 3, "sigma": 0.5, "units": "meters"}) # Compute a difference-of-Gaussians (DOG) kernel. dog = fat.add(skinny) # Print the kernel to see its weights. print("DoG kernel for image sharpening", dog) # Add the DoG convolved image to the original image. sharpened = imageNAIP.add(imageNAIP.convolve(dog)) Map.addLayer(sharpened, {"min": 0, "max": 255}, "DoG edge enhancement") # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- # Begin median example. # Pass a median neighborhood filter using our uniformKernel. median = imageNAIP.reduceNeighborhood( {"reducer": ee.Reducer.median(), "kernel": uniformKernel} ) Map.addLayer(median, {"min": 0, "max": 255}, "Median Neighborhood Filter") # Mode example # Create and display a simple two-class image. veg = imageNAIP.select("N").gt(200) # Display the two-class (binary) result. binaryVis = {"min": 0, "max": 1, "palette": ["black", "green"]} Map.addLayer(veg, binaryVis, "Vegetation categorical image") # Compute the mode in each 5x5 neighborhood and display the result. mode = veg.reduceNeighborhood({"reducer": ee.Reducer.mode(), "kernel": uniformKernel}) Map.addLayer( mode, binaryVis, "Mode Neighborhood Filter on Vegetation categorical image" ) # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

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Map

Map