ee.Kernel.fixed

AI-generated Key Takeaways

• The ee.Kernel.fixed() method creates a Kernel object.

• This method takes optional arguments for width, height, x and y focus locations, and a boolean to normalize the weights, but requires a 2-D list of weights.

• The weights argument should be a 2-D list with dimensions matching the height and width of the kernel.

• Examples in JavaScript and Python demonstrate how to create a fixed kernel using a list of weights.

Creates a Kernel.

Usage	Returns
ee.Kernel.fixed(width, height, weights, x, y, normalize)	Kernel

Argument	Type	Details
width	Integer, default: -1	The width of the kernel in pixels.
height	Integer, default: -1	The height of the kernel in pixels.
weights	List	A 2-D list of [height] x [width] values to use as the weights of the kernel.
x	Integer, default: -1	The location of the focus, as an offset from the left.
y	Integer, default: -1	The location of the focus, as an offset from the top.
normalize	Boolean, default: false	Normalize the kernel values to sum to 1.

Examples

Code Editor (JavaScript)

// Kernel weights.
var weights = [[4, 3, 2, 1, 2, 3, 4],
               [4, 3, 2, 1, 2, 3, 4],
               [4, 3, 2, 1, 2, 3, 4]];

print('A fixed kernel', ee.Kernel.fixed({weights: weights}));

/**
 * Output weights matrix
 *
 * [4, 3, 2, 1, 2, 3, 4]
 * [4, 3, 2, 1, 2, 3, 4]
 * [4, 3, 2, 1, 2, 3, 4]
 */

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)

from pprint import pprint

weights = [[4, 3, 2, 1, 2, 3, 4],
           [4, 3, 2, 1, 2, 3, 4],
           [4, 3, 2, 1, 2, 3, 4]]

print('A fixed kernel:')
pprint(ee.Kernel.fixed(**{'weights': weights}).getInfo())

#  Output weights matrix

#  [4, 3, 2, 1, 2, 3, 4]
#  [4, 3, 2, 1, 2, 3, 4]
#  [4, 3, 2, 1, 2, 3, 4]