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.
| Tool | Description |
|---|---|
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. | |
Stitches together segments that were cut by tile boundaries during the segmentation process. 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. |