This is a fancy way of saying we’re going to use some cool tools to analyze satellite images and figure out what’s where on the ground.
Before anything else, let’s break down what “segmentation” means in this context. Essentially, it involves dividing an image into smaller segments or regions based on certain criteria (like color or texture). This can be useful for all sorts of applications, from identifying crop patterns to monitoring urban development over time.
Now, how we actually do this segmentation using TensorFlow and GEE. First, we need to load in our satellite images into Google Cloud Storage (GCS) using the `gsutil` command:
# This script uses the `gsutil` command to copy satellite images from a local directory to a Google Cloud Storage (GCS) bucket.
# This is necessary for performing segmentation using TensorFlow and GEE.
# The `gsutil cp` command is used to copy files and directories to and from GCS.
# The `-r` flag is used to recursively copy all files and subdirectories within the specified directory.
# The <path/to/images> placeholder should be replaced with the local path to the satellite images.
# The gs://<your-bucket>/ placeholder should be replaced with the name of your GCS bucket.
gsutil cp -r <path/to/images> gs://<your-bucket>/
Once our images are uploaded, we can use GEE to process them and create a new image that highlights areas of interest. For example, let’s say we want to identify crop patterns in a particular region. We could do this by creating an index based on the Normalized Difference Vegetation Index (NDVI), which is calculated using red and near-infrared bands:
// Load Landsat 8 surface reflectance data for a specific area
var landsat = ee.ImageCollection('LANDSAT/LC08/C01') // Creates an image collection object for Landsat 8 surface reflectance data
.filterBounds(<region of interest>) // Filters the image collection by a specific region of interest
.select(['B4', 'B3']) // Selects the red and near-infrared bands from the image collection
.map(function (image) { // Applies a function to each image in the collection
var ndvi = image.normalizedDifference('B4', 'B3') // Calculates the Normalized Difference Vegetation Index (NDVI) using the red and near-infrared bands
return ndvi; // Returns the calculated NDVI index for each image in the collection
});
Once we have our NDVI data, we can use TensorFlow to train a model that will segment the images based on this index:
# Load pre-trained model and input data into memory
model = tf.keras.models.load_model('path/to/pre-trained/model') # Load pre-trained model from specified path
input_data = gee.ImageCollection(<collection of NDVI images>) # Create an image collection of NDVI images
.map(function (image) { # Apply a function to each image in the collection
var ndvi = image.select(['ndvi']) # Select only the NDVI band from the image
return ndvi; # Return the selected NDVI band
})
.toArray() # Convert the collection to an array format for TensorFlow input
.batch(<batch size>) # Batch the data for faster processing in TensorFlow
Finally, we can use GEE to export our segmented images back into Google Cloud Storage:
// Export segmentation results as GeoTIFF files in Google Cloud Storage
var output = model.predict(input_data) // Run prediction on input data using TensorFlow model
.map(function (image, index) {
var id = 'segmented_' + ee.Number(index).toString() // Generate unique ID for each segmentation result
return image.multiply(10000) // Scale output to 32-bit integer format
.addBands({name: 'id', value: id}) // Add an "ID" band with the unique ID for each segmentation result
.clip(<region of interest>) // Clip results to specified region
})
.mosaic() // Merge all segmented images into a single output image
.addBands({name: 'id', description: 'Unique identifier for each segment'}) // Add "ID" band metadata
.set('system:time_start', Date.now().getTime()) // Set creation time metadata
.set('user:email', '<your-email>') // Set email address of user who created the image
.set('user:project', '<your-project>') // Set project name for image
.set('system:footprint_scale', 10) // Set footprint scale to 10 meters (for higher accuracy)
.export(<path/to/output/folder>, 'GEOTIFF', {'crs': <CRS of input images>}) // Export output image as GeoTIFF in specified folder with specified CRS
// Export segmentation results as GeoTIFF files in Google Cloud Storage
var output = model.predict(input_data) // Run prediction on input data using TensorFlow model
.map(function (image, index) {
var id = 'segmented_' + ee.Number(index).toString() // Generate unique ID for each segmentation result
return image.multiply(10000) // Scale output to 32-bit integer format
.addBands({name: 'id', value: id}) // Add an "ID" band with the unique ID for each segmentation result
.clip(<region of interest>) // Clip results to specified region
})
.mosaic() // Merge all segmented images into a single output image
.addBands({name: 'id', description: 'Unique identifier for each segment'}) // Add "ID" band metadata
.set('system:time_start', Date.now().getTime()) // Set creation time metadata
.set('user:email', '<your-email>') // Set email address of user who created the image
.set('user:project', '<your-project>') // Set project name for image
.set('system:footprint_scale', 10) // Set footprint scale to 10 meters (for higher accuracy)
.export(<path/to/output/folder>, 'GEOTIFF', {'crs': <CRS of input images>}) // Export output image as GeoTIFF in specified folder with specified CRS
// The script above exports segmented images back into Google Cloud Storage using GEE.
// The "model.predict" function runs the prediction on the input data using a TensorFlow model.
// The "map" function is used to iterate through each image in the input data and perform the following operations on it.
// The "var id" variable generates a unique ID for each segmentation result.
// The "image.multiply" function scales the output to 32-bit integer format.
// The "addBands" function adds an "ID" band with the unique ID for each segmentation result.
// The "clip" function clips the results to the specified region of interest.
// The "mosaic" function merges all segmented images into a single output image.
// The "addBands" function adds "ID" band metadata.
// The "set" function sets the creation time metadata, email address of the user who created the image, project name, and footprint scale.
// The "export" function exports the output image as a GeoTIFF in the specified folder with the specified CRS.
And that’s it! We now have a segmented image that highlights areas of interest based on our NDVI index. This can be useful for all sorts of applications, from identifying crop patterns to monitoring urban development over time.
Of course, this is just one example there are many other ways you could use TensorFlow and GEE to analyze satellite images and create segmented output data. The possibilities are endless!