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[[["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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06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\u003eGenerates a distance kernel based on the Chebyshev distance, which calculates the greatest distance along any dimension between two pixels.\u003c/p\u003e\n"],["\u003cp\u003eThe kernel can be customized using parameters such as radius, units (pixels or meters), normalization, and magnitude scaling.\u003c/p\u003e\n"],["\u003cp\u003eWhen applied, the kernel assigns weights to neighboring pixels based on their Chebyshev distance from the central pixel, creating a matrix of weights.\u003c/p\u003e\n"],["\u003cp\u003eThe resulting weights matrix can be used in various image processing operations, such as smoothing or neighborhood analysis.\u003c/p\u003e\n"]]],["A Chebyshev distance kernel is generated using `ee.Kernel.chebyshev()` with a specified `radius`. The measurement system can be set to 'pixels' or 'meters' via the `units` argument. The kernel values can be normalized to sum to 1 using `normalize`, and scaled with `magnitude`. The output is a kernel representing the Chebyshev distance, where the greatest distance along any dimension defines the value, and it is presented as a matrix.\n"],null,["# ee.Kernel.chebyshev\n\nGenerates a distance kernel based on Chebyshev distance (greatest distance along any dimension).\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|--------------------------------------------------------------------------|---------|\n| `ee.Kernel.chebyshev(radius, `*units* `, `*normalize* `, `*magnitude*`)` | Kernel |\n\n| Argument | Type | Details |\n|-------------|---------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `radius` | Float | The radius of the kernel to generate. |\n| `units` | String, default: \"pixels\" | The system of measurement for the kernel ('pixels' or 'meters'). If the kernel is specified in meters, it will resize when the zoom-level is changed. |\n| `normalize` | Boolean, default: false | Normalize the kernel values to sum to 1. |\n| `magnitude` | Float, default: 1 | Scale each value by this amount. |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\nprint('A Chebyshev distance kernel', ee.Kernel.chebyshev({radius: 3}));\n\n/**\n * Output weights matrix\n *\n * [3, 3, 3, 3, 3, 3, 3]\n * [3, 2, 2, 2, 2, 2, 3]\n * [3, 2, 1, 1, 1, 2, 3]\n * [3, 2, 1, 0, 1, 2, 3]\n * [3, 2, 1, 1, 1, 2, 3]\n * [3, 2, 2, 2, 2, 2, 3]\n * [3, 3, 3, 3, 3, 3, 3]\n */\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\nfrom pprint import pprint\n\nprint('A Chebyshev distance kernel:')\npprint(ee.Kernel.chebyshev(**{'radius': 3}).getInfo())\n\n# Output weights matrix\n# [3, 3, 3, 3, 3, 3, 3]\n# [3, 2, 2, 2, 2, 2, 3]\n# [3, 2, 1, 1, 1, 2, 3]\n# [3, 2, 1, 0, 1, 2, 3]\n# [3, 2, 1, 1, 1, 2, 3]\n# [3, 2, 2, 2, 2, 2, 3]\n# [3, 3, 3, 3, 3, 3, 3]\n```"]]