Available with Image Analyst license.
Overview
Evaluates changes in pixel values over time using the Continuous Change Detection and Classification (CCDC) method and generates a change analysis raster containing the model results.
For information about the CCDC algorithm, see How Analyze Changes Using CCDC works.
Note:
This raster function is only supported in conjunction with the Detect Change Using Change Analysis function. Use the output layer of the CCDC Analysis function as input to the Detect Change Using Change Analysis function. To produce a raster dataset output, connect the CCDC Analysis function with the Detect Change Using Change Analysis function using the Function Editor, save this as a raster function template, and use it as the input to the Generate Raster From Raster Function geoprocessing tool.
Notes
This raster function can only be used as input to the Detect Change Using Change Analysis raster function. To generate a raster output, connect the CCDC Analysis function to the Detect Change Using Change Analysis function in a raster function template, and use the template as input in the Generate Raster From Raster Function geoprocessing tool. The result is a raster containing information regarding the time at which pixel values changed.
The Bands for Temporal Masking parameter specifies the bands that will be used for cloud, cloud shadow, and snow masking. Because cloud shadow and snow appear dark in the shortwave infrared (SWIR) band, and clouds and snow are bright in the green band, it is recommended that you mask the band indexes for the SWIR and green bands.
The Updating Fitting Frequency (in years) parameter defines how often the time series model will be updated with new observations. Updating a model frequently can be computationally intensive and the benefit can be minimal. For example, if there are 365 slices or clear observations per year in the multidimensional raster, and the updating frequency is for every observation, the processing will be 365 times more computationally intensive compared to updating once per year, but the accuracy may not be higher.
Parameters
| Parameter | Description |
|---|---|
Raster | The input multidimensional raster layer. |
Bands for Detecting Change | The band IDs to use for change detection. If no band IDs are provided, all the bands from the input raster dataset will be used. The ID values should be integers separated by spaces. |
Bands for Temporal Masking | The band IDs of the green band and the SWIR band to be used to mask for clouds, cloud shadow, and snow. If no band IDs are provided, masking will not occur. The ID values should be integers separated by spaces. |
Chi-squared Threshold for Detect Change | The chi-square change probability threshold. If an observation has a calculated change probability that is above this threshold, it is flagged as an anomaly, which is a potential change event. The default value is 0.99. |
Minimum Consecutive Anomaly Observations | The minimum number of consecutive anomaly observations that must occur before an event is considered a change. A pixel must be flagged as an anomaly for the specified number of consecutive time slices before it is considered a true change. The default is 6. |
Updating Fitting Frequency (in years) | The frequency at which to update the time series model with new observations. The default is to update the model once a year. |