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You can compute the gradient of each band of an image withimage.gradient().
For example, the following code computes the gradient magnitude and direction of the
Landsat 8 panchromatic band:
Note thatgradient()outputs two bands: the gradient in the X-direction and the
gradient in the Y-direction. As shown in the example, the two directions can be combined to
get gradient magnitude and direction. The magnitude should look something like Figure 1.
Figure 1. Panchromatic gradient magnitude for the Landsat 8 imagery over the
San Francisco Bay area, California, USA.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[[["\u003cp\u003eThe \u003ccode\u003eimage.gradient()\u003c/code\u003e function in Google Earth Engine calculates the gradient of each band in an image, yielding X and Y direction components.\u003c/p\u003e\n"],["\u003cp\u003eBy combining the X and Y gradient components, you can derive both the gradient magnitude and direction.\u003c/p\u003e\n"],["\u003cp\u003eThe gradient magnitude represents the rate of change in pixel values, while the direction indicates the orientation of this change.\u003c/p\u003e\n"],["\u003cp\u003eVisualizing the gradient can highlight areas of rapid change in the image, such as edges and textures.\u003c/p\u003e\n"],["\u003cp\u003eThis functionality is demonstrated using Landsat 8 panchromatic data, showcasing the gradient over the San Francisco Bay area.\u003c/p\u003e\n"]]],["The `image.gradient()` function computes the gradient of each image band, outputting X and Y-direction gradients. The example loads a Landsat 8 panchromatic band image, calculates the X and Y gradients, then determines the gradient's magnitude by combining the squared X and Y values and the gradient's direction using `atan2` function. Finally, it displays the gradient and its direction, centered on San Francisco. The image gradient magnitude is then illustrated.\n"],null,["# Gradients\n\nYou can compute the gradient of each band of an image with `image.gradient()`.\nFor example, the following code computes the gradient magnitude and direction of the\nLandsat 8 panchromatic band:\n\n### Code Editor (JavaScript)\n\n```javascript\n// Load a Landsat 8 image and select the panchromatic band.\nvar image = ee.Image('LANDSAT/LC08/C02/T1/LC08_044034_20140318').select('B8');\n\n// Compute the image gradient in the X and Y directions.\nvar xyGrad = image.gradient();\n\n// Compute the magnitude of the gradient.\nvar gradient = xyGrad.select('x').pow(2)\n .add(xyGrad.select('y').pow(2)).sqrt();\n\n// Compute the direction of the gradient.\nvar direction = xyGrad.select('y').atan2(xyGrad.select('x'));\n\n// Display the results.\nMap.setCenter(-122.054, 37.7295, 10);\nMap.addLayer(direction, {min: -2, max: 2, format: 'png'}, 'direction');\nMap.addLayer(gradient, {min: -7, max: 7, format: 'png'}, 'gradient');\n```\n\nNote that `gradient()` outputs two bands: the gradient in the X-direction and the\ngradient in the Y-direction. As shown in the example, the two directions can be combined to\nget gradient magnitude and direction. The magnitude should look something like Figure 1.\nFigure 1. Panchromatic gradient magnitude for the Landsat 8 imagery over the San Francisco Bay area, California, USA."]]
