Available with Spatial Analyst license.
With the Segmentation and Classification tools, you can prepare segmented rasters to use in creating classified raster datasets.
The following table lists the available segmentation and classification tools and provides a brief description of each.
Classify a raster dataset based on an Esri Classifier Definition (.ecd) file and raster dataset inputs.
The .ecd file contains all the information needed to perform a specific type of Esri-supported classification. The inputs to this tool need to match the inputs used to generate the required .ecd file.
Computes a confusion matrix with errors of omission and commission, then derives a kappa index of agreement and an overall accuracy between the classified map and the reference data.
Compute a set of attributes associated with your segmented image. The input raster can be a single-band or 3-band, 8-bit segmented image.
Creates randomly sampled points for post-classification accuracy assessment.
Converts a deep learning model to an Esri Classifer Definition file.
Uses a remote sensing image to convert labeled vector or raster data into deep learning training datasets. The output is a folder of image chips, and a folder of metadata files in the specified format.
Generates training samples from seed points, such as accuracy assessment points or training sample points. A typical use case is generating training samples from an existing source, such as a thematic raster or a feature class.
Estimates the accuracy of individual training samples. The cross validation accuracy is computed using the previously generated classification training result in an .ecd file and the training samples. Outputs include a raster dataset containing the misclassified class values and a training sample dataset with the accuracy score for each training sample.
Some regional processes, such as image segmentation, will have inconsistencies near image tile boundaries. This tool corrects segments or objects cut by tile boundaries during the segmentation process performed as a raster function.
This processing step is already included in the Segment Mean Shift tool, therefore it should only be used on a segmented image that was not created from that tool.
Groups together adjacent pixels that have similar spectral characteristics into segments.
Generate an Esri classifier definition (.ecd) file using the Iso Cluster classification definition.
Generate an Esri classifier definition (.ecd) file using the Maximum Likelihood Classifier (MLC) classification definition.
Generate an Esri classifier definition (.ecd) file using the Random Trees classification method.
Generate an Esri classifier definition (.ecd) file using the Support Vector Machine (SVM) classification definition.
Updates the Target field in the attribute table in order to be able to compare reference points to the classified image.