Iso Cluster Unsupervised Classification, Maximum Likelihood Classification and Principal Components Tools
Iso Cluster Unsupervised Classification
How to use Iso Cluster
Unsupervised Classification Tool in Arc Toolbox??
Iso Cluster Unsupervised Classification
Path to access the tool
:
Iso
Cluster Unsupervised Classification Tool, Multivariate Toolset, Spatial Analyst Tools Toolbox
Iso Cluster Unsupervised
Classification
Performs unsupervised
classification on a series of input raster bands using the Iso Cluster and
Maximum Likelihood Classification tools.
Learn more about how the
Interactive Supervised Classification tool works
1. Input raster bands
The input raster bands.
They can be integer or
floating point type.
2. Number of classes
Number of classes into
which to group the cells.
3. Output classified raster
The output classified
raster.
4. Minimum class size (optional)
Minimum number of cells
in a valid class.
The default is 20.
5. Sample interval (optional)
The interval to be used
for sampling.
The default is 10.
6. Output signature file (optional)
The output signature
file.
A .gsg extension must be
specified.
Maximum Likelihood Classification
How to use Maximum
Likelihood Classification Tool in Arc Toolbox??
Maximum Likelihood Classification
Path to access the tool
:
Maximum
Likelihood Classification Tool, Multivariate Toolset, Spatial Analyst Tools Toolbox
Maximum Likelihood Classification
Performs a maximum
likelihood classification on a set of raster bands and creates a classified
raster as output.
1. Input raster bands
The input raster bands.
While the bands can be
integer or floating point type, the signature file only allows integer class
values.
2. Input signature file
The input signature file
whose class signatures are used by the maximum likelihood classifier.
A .gsg extension is
required.
3. Output classified raster
The output classified
raster.
It will be of integer
type.
4. Reject fraction (optional)
Choose a reject
fraction, which determines whether a cell will be classified based on its
likelihood of being correctly assigned to one of the classes. Cells whose
likelihood of being correctly assigned to any of the classes is lower than the
reject fraction will be given a value of NoData in the output classified
raster.
The default is 0.0,
which means that every cell will be classified.
5. A priori probability weighting (optional)
Specifies how a priori probabilities will be determined.
- EQUAL— All classes will have the same a priori probability.
- SAMPLE— A priori probabilities will be proportional to the number of cells in each class relative to the total number of cells sampled in all classes in the signature file.
- FILE—The a priori probabilities will be assigned to each class from an input ASCII a priori probability file.
6. Input a priori probability file (optional)
A text file containing a
priori probabilities for the input signature classes.
An input for the a
priori probability file is only required when the FILE option is used.
The extension for the a
priori file can be .txt or .asc.
7. Output confidence raster (optional)
The output confidence
raster dataset shows the certainty of the classification in 14 levels of
confidence, with the lowest values representing the highest reliability. If
there are no cells classified at a particular confidence level, that confidence
level will not be present in the output confidence raster.
It will be of integer
type.
Principal Components
How to use Principal
Components Tool in Arc Toolbox??
Principal Components
Path to access the tool
:
Principal
Components Tool, Multivariate Toolset, Spatial Analyst Tools Toolbox
Principal Components
Performs Principal
Component Analysis (PCA) on a set of raster bands and generates a single
multiband raster as output.
1. Input raster bands
The input raster bands.
They can be integer or
floating point type.
2. Output multiband raster
The output multiband
raster dataset.
If all of the input
bands are integer type, the output raster bands will be integer. If any of the
input bands are floating point, the output will be floating point.
If the output is an Esri
Grid raster, the name must have less than 10 characters.
3. Number of Principal components (optional)
Number of principal
components.
The number must be
greater than zero and less than or equal to the total number of input raster
bands.
The default is the total
number of rasters in the input.
4. Output data file (optional)
Output ASCII data file
storing principal component parameters.
The output data file
records the correlation and covariance matrices, the eigenvalues and
eigenvectors, the percent variance each eigenvalue captures, and the
accumulative variance described by the eigenvalues.
The extension for the output file can be .txt or .asc.
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