NNCLASS

Description

NNCLASS uses a back-propagation neural-network segment created by NNCREAT and trained by NNTRAIN to classify multispectral imagery.

Parameters

Name	Type	Length	Value range
InputNN: Input neural-network segment *	Raster port	1 - 1
NNLayer: Input neural-network segment *	BIN port	1 - 1
OutputNN: Output classification channel or channels *	Raster port	1 - 254
Probability: Output probability channel	Raster port	0 - 254
Mask: Area mask	Bitmap port	0 - 4
Null Class	String	0 - 1	YES | NO Default: YES
Most Likely Classes Images	String	1 -	1 - 16 Default: 1

* Required parameter

Parameter descriptions

InputNN: Input neural-network segment

The input neural-network segment to be classified.

NNLayer: Input neural-network segment

The neural-network segment (type 180) trained as a classifier by NNTRAIN.

NNCLASS processes the image channels listed in the neural-network segment as the input image data. If an untrained neural network with zero training iterations is specified, NNCLASS will not run.

OutputNN: Output classification channel or channels

The output image channel or channels that receive the classification results. The first channel holds the most likely class, the second channel the second most likely class, and so on. Typically, however, only one output channel is specified.

Up to 254 channels can be handled and up to 48 values can be specified.

At least one channel must be specified. Duplicate channels are not allowed.

Probability: Output probability channel

The output channels in which to store estimated probabilities (values between 0.0 and 100.0) that a pixel belongs to the class to which it was assigned. For each specified output-classification channel (DBOC), you can specify a corresponding probability channel for PROBCHAN.

This function handles up to 254 channels and you can specify up to 48 integer values; however, the number of values you specify cannot exceed that of the output-classification channels (DBOC).

Duplicate channels cannot be defined for DBOC (OutputNN) and PROBCHAN (Probability).

The values generated represent a measure that approximates the true statistical probability. The value generated for a pixel is calculated as the activation value of the output unit for the selected class divided by the activation of all output units for that pixel.

The range of values varies according to the number of output classes: more output classes decrease the range. Higher values, generally, are produced for classes that are well trained; therefore, the probability values produced can be considered relative to the range of probability values produced.

If a second probability channel is specified, it contains the value of the second most active output unit divided by the activation of all output units for that pixel position, and so on.

This parameter is optional.

Mask: Area mask

The bitmap that defines the area to be processed and classified.

If no value is specified, the entire channel is processed.

This parameter is optional.

Null Class

Specifies whether pixels can be assigned to the NULL class (value 0).

Most Likely Classes Images

The required number of most likely classes images. The default value is 1.

Details

NNCLASS classifies multispectral imagery using a back-propagation neural network created by NNCREAT and trained by NNTRAIN.

The input PCIDSK image file (FILE) contains the image channels that receive the output-classification results (DBOC), the input neural-network segment (DBNNS) that defines the trained back-propagation neural network to use, and the input-image channels to process. In addition, output channels for storing the probabilities that each pixel belongs to a class (PROBCHAN) can also be specified.

The classification can be restricted to those pixels beneath a specified bitmap mask. If no mask is specified, each pixel in the image is classified.

The NULLCLAS (Null Class) parameter specifies whether to create a NULL class (YES) where none of the pixels belong to any output class, or whether to assign each pixel to one of the output classes (NO).

Before running NNCLASS, you must first run NNCREAT and NNTRAIN according to the descriptions in the corresponding Help topic for each.