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ee.Algorithms.TemporalSegmentation.Ccdc
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实现持续变化检测和分类时间断点算法。该算法通过迭代地将谐波函数拟合到数据,在图像集合中找到时间断点。系统会为所有输入波段生成拟合系数,但可使用“breakpointBands”实参指定用于检测中断点的波段。
如需了解详情,请参阅 Zhu, Z. 和 Woodcock, C.E.,2014 年。使用所有可用的 Landsat 数据持续检测和分类土地覆盖。Remote sensing of Environment,144,第 152-171 页。
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ee.Algorithms.TemporalSegmentation.Ccdc(collection, breakpointBands, tmaskBands, minObservations, chiSquareProbability, minNumOfYearsScaler, dateFormat, lambda, maxIterations) | 图片 |
参数 | 类型 | 详细信息 |
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collection | ImageCollection | 要运行 CCDC 的图片集合。 |
breakpointBands | 列表,默认值:null | 用于变化检测的波段的名称或指数。如果未指定,则使用所有频段。 |
tmaskBands | 列表,默认值:null | 用于迭代 TMask 云检测的波段的名称或指数。这些波段通常是绿波段和 SWIR1 波段。如果未指定,则不使用 TMask。如果指定,则“tmaskBands”必须包含在“breakpointBands”中。 |
minObservations | 整数,默认值:6 | 标记变化所需的观测次数。 |
chiSquareProbability | 浮点数,默认值:0.99 | 用于检测变化的卡方概率阈值,范围为 [0, 1]。 |
minNumOfYearsScaler | 浮点数,默认值:1.33 | 应用新拟合的最小年限的系数。 |
dateFormat | 整数,默认值:0 | 拟合期间要使用的时间表示形式:0 = jDays,1 = 小数年,2 = 以毫秒为单位的 Unix 时间。每个时间段的开始时间、结束时间和中断时间都将以这种方式进行编码。 |
lambda | 浮点数,默认值:20 | 用于 LASSO 回归拟合的 lambda。如果设置为 0,则使用常规 OLS 而不是 LASSO。 |
maxIterations | 整数,默认值:25000 | LASSO 回归收敛的最大运行次数。如果设置为 0,则使用常规 OLS 而不是 LASSO。 |
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最后更新时间 (UTC):2025-07-26。
[null,null,["最后更新时间 (UTC):2025-07-26。"],[[["\u003cp\u003eImplements the Continuous Change Detection and Classification (CCDC) algorithm to identify temporal breakpoints (changes over time) within an image collection.\u003c/p\u003e\n"],["\u003cp\u003eUses harmonic functions to fit the image data and detect changes based on specified bands and statistical thresholds.\u003c/p\u003e\n"],["\u003cp\u003eOffers customization options such as selecting specific bands for change detection, adjusting sensitivity parameters, and configuring the time format for results.\u003c/p\u003e\n"],["\u003cp\u003eProvides fit coefficients for all input bands, aiding in understanding the nature of the detected changes.\u003c/p\u003e\n"],["\u003cp\u003eRelies on iterative fitting and statistical analysis to pinpoint breakpoints and ensure robust change detection.\u003c/p\u003e\n"]]],[],null,["# ee.Algorithms.TemporalSegmentation.Ccdc\n\nImplements the Continuous Change Detection and Classification temporal breakpoint algorithm. This algorithm finds temporal breakpoints in an image collection by iteratively fitting harmonic functions to the data. Fit coefficients are produced for all input bands, but the bands used for breakpoint detection can be specified with the 'breakpointBands' argument.\n\n\u003cbr /\u003e\n\nFor more details, see Zhu, Z. and Woodcock, C.E., 2014. Continuous change detection and classification of land cover using all available Landsat data. Remote sensing of Environment, 144, pp.152-171.\n\n| Usage | Returns |\n|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------|\n| `ee.Algorithms.TemporalSegmentation.Ccdc(collection, `*breakpointBands* `, `*tmaskBands* `, `*minObservations* `, `*chiSquareProbability* `, `*minNumOfYearsScaler* `, `*dateFormat* `, `*lambda* `, `*maxIterations*`)` | Image |\n\n| Argument | Type | Details |\n|------------------------|-------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `collection` | ImageCollection | Collection of images on which to run CCDC. |\n| `breakpointBands` | List, default: null | The name or index of the bands to use for change detection. If unspecified, all bands are used. |\n| `tmaskBands` | List, default: null | The name or index of the bands to use for iterative TMask cloud detection. These are typically the green band and the SWIR1 band. If unspecified, TMask is not used. If specified, 'tmaskBands' must be included in 'breakpointBands'. |\n| `minObservations` | Integer, default: 6 | The number of observations required to flag a change. |\n| `chiSquareProbability` | Float, default: 0.99 | The chi-square probability threshold for change detection in the range of \\[0, 1\\]. |\n| `minNumOfYearsScaler` | Float, default: 1.33 | Factors of minimum number of years to apply new fitting. |\n| `dateFormat` | Integer, default: 0 | The time representation to use during fitting: 0 = jDays, 1 = fractional years, 2 = unix time in milliseconds. The start, end and break times for each temporal segment will be encoded this way. |\n| `lambda` | Float, default: 20 | Lambda for LASSO regression fitting. If set to 0, regular OLS is used instead of LASSO. |\n| `maxIterations` | Integer, default: 25000 | Maximum number of runs for LASSO regression convergence. If set to 0, regular OLS is used instead of LASSO. |"]]