The new raster layer will contain only the specified subset of bands, and can be used in the Image Classification toolbar. To use only certain bands from an existing dataset for the classification, create a new raster layer for them using the Make Raster Layer tool. To use all bands in an image dataset in the classification, add the image dataset to ArcMap and select the image layer on the Image Classification toolbar. Creating a subset of bands for the classification

To load individual bands to a new multiband image, use the Composite Bands tool. The Image Classification toolbar works with a multiband image layer. The Principal Components tool from the Multivariate toolset allows you to perform principal component analysis. This could be helpful for collecting training samples. By enhancing the first few bands, more details can be seen in the image when it is displayed in ArcMap. The information in the output image is mainly concentrated in the first few bands. Principal component analysis transforms a multiband image to remove correlation among the bands. For example, you can use the Times math tool to multiply the band with a constant value to stretch its value range. If the value range of one band is too small (or too large) relative to the other bands, you can use the mathematical tools in the Spatial Analyst toolbox to stretch it. To have the attributes of each band considered equally, the value range for each band should be similar.

The classification process is sensitive to the range of values in each band. To check the distribution of individual training samples, use the Histograms tool on the Training Sample Manager. To check the distribution of the data in a band, use the interactive Histogram tool on the Spatial Analyst toolbar. The classification analysis is based on the assumption that the band data and the training sample data follow normal distribution. Data exploration and preprocessing Data exploration The detailed steps of the image classification workflow are illustrated in the following chart. Spatial Analyst also provides tools for post-classification processing, such as filtering and boundary cleaning. For unsupervised classification, the signature file is created by running a clustering tool. For supervised classification, the signature file is created using training samples through the Image Classification toolbar. A signature file, which identifies the classes and their statistics, is a required input to this tool. The Maximum Likelihood Classification tool is the main classification method. The Image Classification toolbar provides a user-friendly environment for creating training samples and signature files used in supervised classification. With the ArcGIS Spatial Analyst extension, the Multivariate toolset provides tools for both supervised and unsupervised classification.

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