Available with Spatial Analyst license.
Multivariate statistical geoprocessing functions allow the exploration of relationships among many different types of attributes. There are two types of multivariate analysis available: Classification (both Supervised and Unsupervised) and Principal Component Analysis (PCA).
The goal of classification is to assign each cell in a study area to a class or category. With Supervised classification, you have a specific knowledge about the study area and can identify representative areas, or samples, of each class. Unsupervised classification uses naturally occurring statistical groupings in the data to determine the clusters into which the data will be classified.
The following topics provide background information on the theoretical aspects of these geoprocessing functions as well as some examples of their implementation.
- Learn about multivariate classification
- Learn how to produce signature files and class and cluster analysis
- Learn how to evaluate classes and clusters
- Learn how to perform the classification
The general procedure for both Supervised and Unsupervised classification follows:
- Identify the input bands.
- Create the classes or clusters.
The following geoprocessing functions can be used: Create Signatures, Iso Cluster, or Sample from the Extraction geoprocessing functions category.
- Evaluate and edit the classes or clusters.
Use the Dendrogram or Edit Signatures geoprocessing functions.
- Perform the classification.
Use the Maximum Likelihood Classification or Class Probability geoprocessing functions.
The Iso Cluster Unsupervised Classification geoprocessing function allows you to conveniently perform an unsupervised classification by combining steps 1, 2, and 4 described above into a single geoprocessing function.
To eliminate redundancy in the data and make it more interpretable, you can transform your multivariate data through PCA.
The following table lists the available geoprocessing functions and provides a brief description of each.
Calculates the statistics for a set of raster bands.
Creates a multiband raster of probability bands, with one band being created for each class represented in the input signature file.
Creates an ASCII signature file of classes defined by input sample data and a set of raster bands.
Constructs a tree diagram (dendrogram) showing attribute distances between sequentially merged classes in a signature file.
Edits and updates a signature file by merging, renumbering, and deleting class signatures.
Uses an isodata clustering algorithm to determine the characteristics of the natural groupings of cells in multidimensional attribute space and stores the results in an output ASCII signature file.
Performs unsupervised classification on a series of input raster bands using the Iso Cluster and Maximum Likelihood Classification tools.
Performs a maximum likelihood classification on a set of raster bands and creates a classified raster as output.
Performs Principal Component Analysis (PCA) on a set of raster bands and generates a single multiband raster as output.