Spectral transformations

AI-generated Key Takeaways

• Earth Engine provides various spectral transformation methods such as normalizedDifference(), unmix(), rgbToHsv(), and hsvToRgb().

• Pan sharpening improves image resolution by combining a multiband image with a higher-resolution panchromatic image, often using rgbToHsv() and hsvToRgb().

• Spectral unmixing, implemented as image.unmix(), separates a pixel's spectrum into fractional components based on defined endmembers.

Run in Google Colab

View source on GitHub

There are several spectral transformation methods in Earth Engine. These include instance methods on images such as normalizedDifference(), unmix(), rgbToHsv() and hsvToRgb().

Pan sharpening

Pan sharpening improves the resolution of a multiband image through enhancement provided by a corresponding panchromatic image with finer resolution. The rgbToHsv() and hsvToRgb() methods are useful for pan sharpening.

Code Editor (JavaScript)

// Load a Landsat 8 top-of-atmosphere reflectance image.
var image = ee.Image('LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140318');
Map.addLayer(
    image,
    {bands: ['B4', 'B3', 'B2'], min: 0, max: 0.25, gamma: [1.1, 1.1, 1]},
    'rgb');

// Convert the RGB bands to the HSV color space.
var hsv = image.select(['B4', 'B3', 'B2']).rgbToHsv();

// Swap in the panchromatic band and convert back to RGB.
var sharpened = ee.Image.cat([
  hsv.select('hue'), hsv.select('saturation'), image.select('B8')
]).hsvToRgb();

// Display the pan-sharpened result.
Map.setCenter(-122.44829, 37.76664, 13);
Map.addLayer(sharpened,
             {min: 0, max: 0.25, gamma: [1.3, 1.3, 1.3]},
             'pan-sharpened');

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

Colab (Python)

# Load a Landsat 8 top-of-atmosphere reflectance image.
image = ee.Image('LANDSAT/LC08/C02/T1_TOA/LC08_044034_20140318')

# Convert the RGB bands to the HSV color space.
hsv = image.select(['B4', 'B3', 'B2']).rgbToHsv()

# Swap in the panchromatic band and convert back to RGB.
sharpened = ee.Image.cat(
    [hsv.select('hue'), hsv.select('saturation'), image.select('B8')]
).hsvToRgb()

# Define a map centered on San Francisco, California.
map_sharpened = geemap.Map(center=[37.76664, -122.44829], zoom=13)

# Add the image layers to the map and display it.
map_sharpened.add_layer(
    image,
    {
        'bands': ['B4', 'B3', 'B2'],
        'min': 0,
        'max': 0.25,
        'gamma': [1.1, 1.1, 1],
    },
    'rgb',
)
map_sharpened.add_layer(
    sharpened,
    {'min': 0, 'max': 0.25, 'gamma': [1.3, 1.3, 1.3]},
    'pan-sharpened',
)
display(map_sharpened)

Spectral unmixing

Spectral unmixing is implemented in Earth Engine as the image.unmix() method. (For more flexible methods, see the Array Transformations page). The following is an example of unmixing Landsat 5 with predetermined urban, vegetation and water endmembers:

Code Editor (JavaScript)

// Load a Landsat 5 image and select the bands we want to unmix.
var bands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7'];
var image = ee.Image('LANDSAT/LT05/C02/T1/LT05_044034_20080214')
  .select(bands);
Map.setCenter(-122.1899, 37.5010, 10); // San Francisco Bay
Map.addLayer(image, {bands: ['B4', 'B3', 'B2'], min: 0, max: 128}, 'image');

// Define spectral endmembers.
var urban = [88, 42, 48, 38, 86, 115, 59];
var veg = [50, 21, 20, 35, 50, 110, 23];
var water = [51, 20, 14, 9, 7, 116, 4];

// Unmix the image.
var fractions = image.unmix([urban, veg, water]);
Map.addLayer(fractions, {}, 'unmixed');

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

Colab (Python)

# Load a Landsat 5 image and select the bands we want to unmix.
bands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7']
image = ee.Image('LANDSAT/LT05/C02/T1/LT05_044034_20080214').select(bands)

# Define spectral endmembers.
urban = [88, 42, 48, 38, 86, 115, 59]
veg = [50, 21, 20, 35, 50, 110, 23]
water = [51, 20, 14, 9, 7, 116, 4]

# Unmix the image.
fractions = image.unmix([urban, veg, water])

# Define a map centered on San Francisco Bay.
map_fractions = geemap.Map(center=[37.5010, -122.1899], zoom=10)

# Add the image layers to the map and display it.
map_fractions.add_layer(
    image, {'bands': ['B4', 'B3', 'B2'], 'min': 0, 'max': 128}, 'image'
)
map_fractions.add_layer(fractions, None, 'unmixed')
display(map_fractions)

Figure 1. Landsat 5 imagery unmixed to urban (red), vegetation (green) and water (blue) fractions. San Francisco bay area, California, USA.