| Label | Explanation | Data Type |
Input Raster or Feature Class Data | The input classification image or other thematic GIS reference data. The input can be a raster or feature class. Typical data is a classification image of a single band, integer data type. If using polygons as input, only use those that are not used as training samples. They can also be GIS land-cover data in shapefile or feature class format. | Raster Layer; Mosaic Layer; Feature Layer |
Output Accuracy Assessment Points | The output point shapefile or feature class that contains the random points to be used for accuracy assessment. | Feature Class |
Target Field (Optional) | Specifies whether the input data is a classified image or ground truth data.
| String |
Number of Random Points (Optional) | The total number of random points that will be generated. The actual number may exceed but never fall below this number, depending on sampling strategy and number of classes. The default number of randomly generated points is 500. | Long |
Sampling Strategy (Optional) | Specifies the sampling scheme that will be used.
| String |
Dimension Field for Feature Class (Optional) | A field that defines the dimension (time) of the features. This parameter is used only if the classification result is a multidimensional raster and you want to generate assessment points from a feature class, such as land classification polygons for multiple years. | Field |
Available with Spatial Analyst license.
Available with Image Analyst license.
Summary
Creates randomly sampled points for postclassification accuracy assessment.
A common practice is to randomly select hundreds of points and label their classification types by referencing reliable sources, such as field work or human interpretation of high-resolution imagery. The reference points are then compared with the classification results at the same locations.
Usage
This tool creates a set of random points and assigns a class to them based on reference data.
This tool can also assign a class to the set of points using a previously classified image or a feature class.
The accuracy assessment workflow usually uses the following three tools in this order: Create Accuracy Assessment Points, Update Accuracy Assessment Points, and Compute Confusion Matrix.
When a polygon feature class is used for training or accuracy assessment, the feature class must have a Classvalue or value field that has a unique integer value for each class. For example, a polygon feature class with three different classes can have values such as [1, 2, 3] or [10, 20, 40].
When the Input Raster or Feature Class Data parameter value is a multidimensional raster, the random points generated will use all images in the time series with a date field indicating the image the points are generated from. To generate points for a subset of images, use the Make Multidimensional Raster Layer tool to create an intermediate layer, or the Subset Multidimensional Raster tool to create an intermediate dataset before using this tool.
After running this tool, you can edit the table to manually assign a class to some or all of the points.
Parameters
CreateAccuracyAssessmentPoints(in_class_data, out_points, {target_field}, {num_random_points}, {sampling}, {polygon_dimension_field})| Name | Explanation | Data Type |
in_class_data | The input classification image or other thematic GIS reference data. The input can be a raster or feature class. Typical data is a classification image of a single band, integer data type. If using polygons as input, only use those that are not used as training samples. They can also be GIS land-cover data in shapefile or feature class format. | Raster Layer; Mosaic Layer; Feature Layer |
out_points | The output point shapefile or feature class that contains the random points to be used for accuracy assessment. | Feature Class |
target_field (Optional) |
Specifies whether the input data is a classified image or ground truth data.
| String |
num_random_points (Optional) | The total number of random points that will be generated. The actual number may exceed but never fall below this number, depending on sampling strategy and number of classes. The default number of randomly generated points is 500. | Long |
sampling (Optional) | Specifies the sampling scheme that will be used.
| String |
polygon_dimension_field (Optional) | A field that defines the dimension (time) of the features. This parameter is used only if the classification result is a multidimensional raster and you want to generate assessment points from a feature class, such as land classification polygons for multiple years. | Field |