3D Analyst Tools ArcToolbox

Segment Mean Shift, Train ISO Cluster Classifier and Maximum Likelihood Classifier Tools

Segment Mean Shift

How to use Segment Mean Shift Tool in Arc Toolbox??

Segment Mean Shift

Path to access the tool

:

Segment Mean Shift Tool, Segmentation and Classification Toolset, Spatial Analyst Tools Toolbox

 

Segment Mean Shift

Groups into segments adjacent pixels that have similar spectral characteristics.

1.    Input Raster

Select the raster dataset you want to segment. This can be a multispectral or grayscale image.

2.    Output Raster Dataset

Specify a name and extension for the output dataset.

If your input was a multispectral image, the output will be an 8-bit RGB image. If the input was a grayscale image, the output will be an 8-bit grayscale image.

3.    Spectral Detail [1..20] (optional)

Specifies the level of importance given to the spectral differences of features in your imagery.

Valid values range from 1.0 to 20.0. A higher value is appropriate when you have features you want to classify separately but have similar spectral characteristics. Smaller values create spectrally smoother outputs. For example, with higher spectral detail in a forested scene, you will have greater discrimination between the tree species.

4.    Spatial Detail [1..20] (optional)

Specifies the level of importance given to the proximity between features in your imagery.

Valid values range from 1.0 to 20. A higher value is appropriate for a scene where your features of interest are small and clustered together. Smaller values create spatially smoother outputs. For example, in an urban scene, you could classify an impervious surface using a smaller spatial detail, or you could classify buildings and roads as separate classes using a higher spatial detail.

5.    Minimum Segment Size In Pixels (optional)

Merge segments smaller than this size with their best fitting neighbor segment. This is related to the minimum mapping unit for your project.

Units are in pixels.

6.    Band Indexes (optional)

Select the bands you want to use to segment your imagery, separated by a space. If no band indexes are specified, they are chosen by the following criteria:

1. If the raster has only 3 bands, those 3 bands are used
2. If the raster has more than 3 bands, the tool assigns the red, green and blue bands according to the raster's properties.
3. If the red, green and blue bands are not identified in the raster dataset's properties, bands 1, 2, and 3 are used.

The band order will not change the result.

You want to select bands that offer the most differentiation between the features of interest.

Train ISO Cluster Classifier

How to use Train ISO Cluster Classifier Tool in Arc Toolbox??

Train ISO Cluster Classifier

Path to access the tool

:

Train ISO Cluster Classifier Tool, Segmentation and Classification Toolset, Spatial Analyst Tools Toolbox

 

Train ISO Cluster Classifier

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

This tool performs an unsupervised classification.

1.    Input Raster

The raster dataset to classify.

2.    Max Number Of Classes / Clusters

Maximum number of desired classes to group pixels or segments. This should be set to be greater than the number of classes in your legend.

It is possible that you will get fewer classes than what you specified for this parameter. If you need more, increase this value and aggregate classes after the training process is complete.

3.    Output Classifier Definition File

The output JSON file that contains attribute information, statistics, hyperplane vectors, and other information for the classifier. An .ecd file is created.

4.    Additional Input Raster (optional)

Incorporate ancillary raster datasets, such as a multispectral image or a DEM, to generate attributes and other required information for classification. This parameter is optional.

5.    Max Number Of Iterations (optional)

The maximum number of iterations for the clustering process to run.

The recommended range is between 10 and 20 iterations. Increasing this value will linearly increase the processing time.

6.    Max Number of Cluster Merges per Iteration (optional)

The maximum number of cluster merges per iteration. Increasing the number of merges will reduce the number of classes that are created. A lower value will result in more classes.

7.    Max Merge Distance (optional)

The maximum distance between cluster centers in feature space. Increasing the distance will allow more clusters to merge, resulting in fewer classes. A lower value will result in more classes. Values from 0 to 5 tend to give the best results.

8.    Min Number Of Samples Per Cluster (optional)

The minimum number of pixels or segments in a valid cluster or class.

The default value of 20 has shown to be effective in creating statistically significant classes. You can increase this number for more robust classes; however, it may limit the overall number of classes that are created.

9.    Skip Factor (optional)

Number of pixels to skip for a pixel image input. If a segmented image is an input, specify the number of segments to skip.

10. Segment Attributes Used (optional)

Specifies the attributes to be included in the attribute table associated with the output raster.

1. COLOR—The RGB color values are derived from the input raster, on a per-segment basis.
2. MEAN—The average digital number (DN), derived from the optional pixel image, on a per-segment basis.
3. STD—The standard deviation, derived from the optional pixel image, on a per-segment basis.
4. COUNT—The number of pixels comprising the segment, on a per-segment basis.
5. COMPACTNESS—The degree to which a segment is compact or circular, on a per-segment basis. The values range from 0 to 1, where 1 is a circle.
6. RECTANGULARITY—The degree to which the segment is rectangular, on a per-segment basis. The values range from 0 to 1, where 1 is a rectangle.

This parameter is only active if the Segmented key property is set to true on the input raster. If the only input to the tool is a segmented image, the default attributes are COLOR, COUNT, COMPACTNESS, and RECTANGULARITY. If an Additional Input Raster is included as an input with a segmented image, MEAN and STD are also available attributes.

Train Maximum Likelihood Classifier

How to use Train Maximum Likelihood Classifier Tool in Arc Toolbox??

Train Maximum Likelihood Classifier

Path to access the tool

:

Train Maximum Likelihood Classifier Tool, Segmentation and Classification Toolset, Spatial Analyst Tools Toolbox

 

Train Maximum Likelihood Classifier

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

1.    Input Raster

The raster dataset to classify.

2.    Input Training Sample Features

The training sample file or layer that delineates your training sites.

These can be either shapefiles or feature classes that contain your training samples. The following field names are required in the training sample file:

1. classname—A text field indicating the name of the class category.
2. classvalue—A long integer field containing the integer value for each class category.

3.    Output Classifier Definition File ملف تعريف مصنف المخرج

The output JSON file that contains attribute information, statistics, hyperplane vectors, and other information for the classifier. An .ecd file is created.

4.    Additional Input Raster (optional)

Optionally incorporate ancillary raster datasets, such as a segmented image or DEM.

5.    Segment Attributes Used (optional)

Specifies the attributes to be included in the attribute table associated with the output raster.

1. COLOR—The RGB color values are derived from the input raster, on a per-segment basis.
2. MEAN—The average digital number (DN), derived from the optional pixel image, on a per-segment basis.
3. STD—The standard deviation, derived from the optional pixel image, on a per-segment basis.
4. COUNT—The number of pixels comprising the segment, on a per-segment basis.
5. COMPACTNESS—The degree to which a segment is compact or circular, on a per-segment basis. The values range from 0 to 1, where 1 is a circle.
6. RECTANGULARITY—The degree to which the segment is rectangular, on a per-segment basis. The values range from 0 to 1, where 1 is a rectangle.

This parameter is only active if the Segmented key property is set to true on the input raster. If the only input to the tool is a segmented image, the default attributes are COLOR, COUNT, COMPACTNESS, and RECTANGULARITY. If an Additional Input Raster is included as an input with a segmented image, MEAN and STD are also available attributes.