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Earth Engine has several special methods for estimating spatial texture. When
the image is discrete valued (not floating point), you can useimage.entropy()to compute theentropyin a neighborhood:
Note that the NIR band is scaled to 8-bits prior to callingentropy()since the entropy computation takes discrete valued inputs. The non-zero elements in the
kernel specify the neighborhood.
Another way to measure texture is with a gray-level co-occurrence matrix (GLCM). Using
the image and kernel from the previous example, compute the GLCM-based contrast as follows:
Local measures of spatial association such as Geary’s C(Anselin 1995)can be computed in Earth Engine usingimage.neighborhoodToBands(). Using the image from the previous example:
[[["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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06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\u003eEarth Engine provides various methods for estimating spatial texture, including entropy, GLCM, and Geary's C.\u003c/p\u003e\n"],["\u003cp\u003eThe \u003ccode\u003eimage.entropy()\u003c/code\u003e method calculates entropy in a neighborhood for discrete-valued images.\u003c/p\u003e\n"],["\u003cp\u003eGray-level co-occurrence matrix (GLCM) texture measures, such as contrast, can be computed using \u003ccode\u003eimage.glcmTexture()\u003c/code\u003e.\u003c/p\u003e\n"],["\u003cp\u003eSpatial association, like Geary's C, can be calculated by using \u003ccode\u003eimage.neighborhoodToBands()\u003c/code\u003e to analyze neighborhood relationships.\u003c/p\u003e\n"],["\u003cp\u003eNeighborhood statistics, including standard deviation, offer another approach to texture analysis, as demonstrated on the Statistics of Image Neighborhoods page.\u003c/p\u003e\n"]]],["Earth Engine provides tools for estimating spatial texture. `image.entropy()` calculates entropy in a discrete-valued image neighborhood, using a kernel to define that neighborhood. `image.glcmTexture()` computes gray-level co-occurrence matrix (GLCM) texture measures like contrast. `image.neighborhoodToBands()` enables the computation of spatial associations like Geary's C, using a specified kernel and neighborhood weights. Standard deviation can also be used as explained in the earth-engine documentation.\n"],null,["# Texture\n\nEarth Engine has several special methods for estimating spatial texture. When\nthe image is discrete valued (not floating point), you can use `image.entropy()`\nto compute the\n[entropy](http://en.wikipedia.org/wiki/Entropy_(information_theory))\nin a neighborhood:\n\n### Code Editor (JavaScript)\n\n```javascript\n// Load a high-resolution NAIP image.\nvar image = ee.Image('USDA/NAIP/DOQQ/m_3712213_sw_10_1_20140613');\n\n// Zoom to San Francisco, display.\nMap.setCenter(-122.466123, 37.769833, 17);\nMap.addLayer(image, {max: 255}, 'image');\n\n// Get the NIR band.\nvar nir = image.select('N');\n\n// Define a neighborhood with a kernel.\nvar square = ee.Kernel.square({radius: 4});\n\n// Compute entropy and display.\nvar entropy = nir.entropy(square);\nMap.addLayer(entropy,\n {min: 1, max: 5, palette: ['0000CC', 'CC0000']},\n 'entropy');\n```\n\nNote that the NIR band is scaled to 8-bits prior to calling `entropy()`\nsince the entropy computation takes discrete valued inputs. The non-zero elements in the\nkernel specify the neighborhood.\n\nAnother way to measure texture is with a gray-level co-occurrence matrix (GLCM). Using\nthe image and kernel from the previous example, compute the GLCM-based contrast as follows:\n\n### Code Editor (JavaScript)\n\n```javascript\n// Compute the gray-level co-occurrence matrix (GLCM), get contrast.\nvar glcm = nir.glcmTexture({size: 4});\nvar contrast = glcm.select('N_contrast');\nMap.addLayer(contrast,\n {min: 0, max: 1500, palette: ['0000CC', 'CC0000']},\n 'contrast');\n```\n\nMany measures of texture are output by `image.glcm()`. For a complete\nreference on the outputs, see\n[Haralick et al.\n(1973)](http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4309314&tag=1) and\n[Conners et al.\n(1984)](http://www.sciencedirect.com/science/article/pii/0734189X8490197X).\n\nLocal measures of spatial association such as Geary's C\n[(Anselin 1995)](http://onlinelibrary.wiley.com/doi/10.1111/j.1538-4632.1995.tb00338.x/abstract) can be computed in Earth Engine using\n`image.neighborhoodToBands()`. Using the image from the previous example:\n\n### Code Editor (JavaScript)\n\n```javascript\n// Create a list of weights for a 9x9 kernel.\nvar row = [1, 1, 1, 1, 1, 1, 1, 1, 1];\n// The center of the kernel is zero.\nvar centerRow = [1, 1, 1, 1, 0, 1, 1, 1, 1];\n// Assemble a list of lists: the 9x9 kernel weights as a 2-D matrix.\nvar rows = [row, row, row, row, centerRow, row, row, row, row];\n// Create the kernel from the weights.\n// Non-zero weights represent the spatial neighborhood.\nvar kernel = ee.Kernel.fixed(9, 9, rows, -4, -4, false);\n\n// Convert the neighborhood into multiple bands.\nvar neighs = nir.neighborhoodToBands(kernel);\n\n// Compute local Geary's C, a measure of spatial association.\nvar gearys = nir.subtract(neighs).pow(2).reduce(ee.Reducer.sum())\n .divide(Math.pow(9, 2));\nMap.addLayer(gearys,\n {min: 20, max: 2500, palette: ['0000CC', 'CC0000']},\n \"Geary's C\");\n```\n\nFor an example of using neighborhood standard deviation to compute image texture, see the\n[Statistics of Image Neighborhoods page](/earth-engine/guides/reducers_reduce_neighborhood)."]]
