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An overview of the Segmentation and Classification toolset

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 Raster

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.

Compute Confusion Matrix

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 Segment Attributes

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.

Create Accuracy Assessment Points

Creates randomly sampled points for post-classification accuracy assessment.

Deep Learning Model To Ecd

Converts a deep learning model to an Esri Classifer Definition file.

Export Training Data

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.

Generate Training Samples From Seed Points

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.

Inspect Training Samples

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.

Remove Raster Segment Tiling Artifacts

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.

Segment Mean Shift

Groups together adjacent pixels that have similar spectral characteristics into segments.

Train Iso Cluster Classifier

Generate an Esri classifier definition (.ecd) file using the Iso Cluster classification definition.

Train Maximum Likelihood Classifier

Generate an Esri classifier definition (.ecd) file using the Maximum Likelihood Classifier (MLC) classification definition.

Train Random Trees Classifier

Generate an Esri classifier definition (.ecd) file using the Random Trees classification method.

Train Support Vector Machine Classifier

Generate an Esri classifier definition (.ecd) file using the Support Vector Machine (SVM) classification definition.

Update Accuracy Assessment Points

Updates the Target field in the attribute table in order to be able to compare reference points to the classified image.

Tools of the Segmentation and Classification toolset

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