The result of the classification is a theme map directed to a specified database image channel (DBOC). A theme map encodes each class with a unique grey level. The grey-level value used to encode a class is specified when the class signature is created (VALU for CSG). If the theme map is later directed to the display, a pseudo-colour table should be loaded so that each class is represented by a different colour. If more than 1 output channel is specified, the 2nd, 3rd, ..., nth most likely classes will be stored in the 2nd, 3rd, ..., nth output channels, respectively. Up to 16 output channels can be specified. The number of output channels cannot be more than the number of signatures. If parallelepiped classification is chosen, only 1 output channel can be specified.
After the classification is completed, one may want to know the "a posteriori" probabilities that the pixel belongs to each of the training classes, given that the pixel has the feature value X. PROBCHAN is a parameter for this purpose. It outputs the a posteriori probability values of each pixel belonging to a certain class. If more than one DBOC and PROBCHAN are specified, the 2nd, 3rd,.. nth PROBCHAN channel will store the corresponding "a posteriori" probability for the 2nd, 3rd,.. nth DBOC channel for each pixel. The number of PROBCHAN values can be less than or equal to the number of DBOC values. PROBCHAN can only be specified when MAXL="FULL" (full maximum likelihood classification). Since probability values are real numbers, 32-bit real channels are recommended for PROBCHAN. New channels can be created using the PCIMOD program. Users can see the classification and "a posteriori" probability results for each pixel using the VATT or NUM program.
The NULLCLAS parameter allows the user to specify whether every pixel should be classified. If this option is "YES" then a pixel is assigned to a class only if it is within the gaussian threshold specified for the class. If it is not within any threshold, it is assigned to the NULL (0) class. If the option is "NO" then the thresholds are ignored and every pixel will be assigned to the most probable class (i.e., nearest class based on Mahalanobis distance).
If a report device is selected, MLC generates a classification report. Sub-area totalizations can be obtained by using MLR.
There are three types of multi-class classifiers available: the PARAllelepiped classifier (MAXL="PARA"); the FULL maximum likelihood classifier (MAXL="FULL"); and the TIES classifier which is a parallelepiped classifier which uses full maximum likelihood classification in the event of class ties.
The parallelepiped classifier is typically used when speed is required. The draw back is (in many cases) poor accuracy and a large number of pixels classified as ties (or overlap, class 255).
The maximum likelihood classifier is considered to give more `accurate' results than parallelepiped classification however it is much slower due to extra computations. We put the word `accurate' in quotes because this assumes that classes in the input data have a Gaussian distribution and that signatures were well selected; this is not always a safe assumption.
This type of classification is an attempt to gain the speed of the parallelepiped classifier while eliminating the large number of pixels classed as ties (overlap).
Typically the Ties approach is used as a preliminary to the full maximum likelihood classification.
CLASS A
+-------------------.....-+ ... outlines Gaussian
| a ... .| hyperellipsoid
| .. ..|
| .. .. | +--+ outlines bounding
| .. b .. | parallelepiped
| .. +-----------------------.....-+
| .. | c .. | ... .| pixel PARA FULL TIES
|.. | .. d ..| ..| a A 0 A
|. | .. .. e | .. | b A A A
|.. .| .. | .. | c tie A A
+-.....-------------------+ f .. | d tie 0 0
| .. .. g | e tie B B
h | .. .. | f B B B
|.. ... | g B 0 B
|. .. | h 0 0 0
CLASS B +--.....----------------------+