Disponible con licencia de Image Analyst.
Resumen
Evalúa los cambios en los valores de píxel a lo largo del tiempo utilizando el método Clasificación y detección de cambios continuas (CCDC) y genera un ráster multidimensional que contiene los resultados del modelo.
Uso
The Continuous Change Detection and Classification (CCDC) algorithm is a method for identifying change in pixel values over time. It was originally developed for a time series of multiband Landsat imagery and is used to detect change and classify land cover before and after the change occurred. This tool can be used with imagery from supported sensors and can also be used to detect change in single band rasters. For example, this tool can be used to detect changes in a time series of NDVI rasters to identify deforestation events.
The input multidimensional raster must have at least 12 slices, spanning at least 1 year.
It is recommended that you remove cloud and cloud shadow mask using a QA band before running this tool.
To explore the changes calculated in the output change analysis raster, create a temporal profile chart. Generate graphs for various locations in the change analysis raster to see when changes have occurred. For pixels that have changed, the graph will show breaks in the model where the harmonic regression model for the pixel values over time shifted to a new model, indicating a change. You can hover the pointer over the points on the graph to identify the date the model changed.
The output change analysis raster is a multidimensional raster in which each slice is a multiband raster composed of the time series model coefficients, root mean square error (RMSE), and the observed changes. The number of slices in the output matches the number of slices in the input. It can be used as the input to the Detect Change Using Change Analysis Raster tool, which generates a raster containing change information for each pixel.
The output change analysis raster can also be used for classification. Run this tool to generate a change analysis raster. Then create training samples with a time field to indicate the time at which the sample represents land cover. Next, run a training tool to generate a classifier definition file (.ecd). Finally, run the Classify Raster tool with the .ecd file and the change analysis raster as inputs to generate a multidimensional classified raster.
El parámetro Bandas de enmascaramiento temporal especifica las bandas que se van a utilizar en el enmascaramiento de nubes, sombras de nubes y nieve. Como las sombras de las nubes y la nieve se muestran muy oscuras en la banda infrarroja de onda corta (SWIR), y las nubes y la nieve aparecen muy brillantes en la banda verde, se recomienda enmascarar los índices de banda correspondientes a las bandas SWIR y verde.
El parámetro Actualizando la frecuencia de ajuste (en años) define la frecuencia con la que se actualiza el modelo de serie temporal con nuevas observaciones. Una actualización frecuente del modelo puede suponer un coste informático y reportar muy pocos beneficios. Por ejemplo, si hay 365 divisiones u observaciones claras al año en un ráster multidimensional y se actualiza con cada observación, el procesamiento será 365 veces más costoso desde el punto de vista informático que la actualización anual; además, la precisión podría no ser mayor.
This tool may take a long time to run and requires significant disk space to store the results. To improve processing time and reduce the amount of storage space, the following steps are recommended:
- Turn off the Pyramid environment. Uncheck the Build Pyramids box in the Environment pane or set the environment to NONE in Python.
- Set the Compression environment to LERC and set Max error to 0.000001.
- If you expect to run the Detect Change Using Change Analysis Raster tool on the output of this tool multiple times, it is recommended that you build a multidimensional transpose on the result.
Esta herramienta produce un dataset ráster multidimensional en formato de ráster de nube (CRF). En este momento, no se admite ningún otro formato de salida.
Sintaxis
AnalyzeChangesUsingCCDC(in_multidimensional_raster, {bands}, {tmask_bands}, {chi_squared_threshold}, {min_anomaly_observations}, {update_frequency})| Parámetro | Explicación | Tipo de datos |
in_multidimensional_raster | El dataset ráster multidimensional de entrada. | Raster Dataset; Raster Layer; Mosaic Dataset; Mosaic Layer; Image Service |
bands [bands,...] (Opcional) | 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. | Long |
tmask_bands [tmask_bands,...] (Opcional) | The band IDs to be used in the temporal mask (Tmask). It is recommended that you use the green band and the SWIR band. If no band IDs are provided, no masking will occur. | Long |
chi_squared_threshold (Opcional) | The chi-square statistic 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. | Double |
min_anomaly_observations (Opcional) | 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 value is 6. | Long |
update_frequency (Opcional) | The frequency, in years, at which to update the time series model with new observations. The default value is 1. | Double |
Valor de retorno
| Nombre | Explicación | Tipo de datos |
| out_ccdc_result | El dataset ráster multidimensional de formato de ráster de nube (CRF) de salida. The output change analysis raster containing model information from the CCDC analysis. | Raster |
Muestra de código
This example performs continuous change detection on 30 years of monthly NDVI rasters. Only one band is used in the change detection and the chi-squared probability threshold is 0.90.
# Import system modules
import arcpy
from arcpy.ia import *
# Check out the ArcGIS Image Analyst extension license
arcpy.CheckOutExtension("ImageAnalyst")
changeAnalysisRaster = arcpy.ia.AnalyzeChangesUsingCCDC(
"Monthly_NDVI_30_years.crf", [0], [], 0.90, 6, 1);
# Save output
changeAnalysisRaster.save(r"C:\data\NDVI_ChangeAnalysis.crf")This example performs continuous change detection on a time series of Landsat 7 images, with bands 3 and 7 (indexed at 2 and 6) used for a snow, cloud, and cloud shadow mask.
# Import system modules
import arcpy
from arcpy.ia import *
# Check out the ArcGIS Image Analyst extension license
arcpy.CheckOutExtension("ImageAnalyst")
# Define input parameters
in_multidimensional = r"C:\data\Landsat_time_series.crf"
change_bands = [0,1,2,3,4,5,6]
tmask_bands = [2,6]
chi_sq_threshold = 0.99
min_consecutive_observations = 3
update_frequency = 1
# Execute
changeAnalysisRaster = arcpy.ia.AnalyzeChangesUsingCCDC(
in_multidimensional, change_bands, tmask_bands, chi_sq_threshold,
min_consecutive_observations, update_frequency)
# Save output
changeAnalysisRaster.save(r"C:\data\Landsat_ChangeAnalysis.crf")Entornos
Información de licenciamiento
- Basic: Requiere Image Analyst
- Standard: Requiere Image Analyst
- Advanced: Requiere Image Analyst