with a practical amount of hardware, however, requires that the pre- 
processing and feature extraction mechanism extract the essence of the 
patterns to be identified. 
In the design of the pattern recognition system, five major 
functional divisions must be considered. These phases are illustrated 
in the block diagram of Figure 13. The input pattern or pixel is a 
vector quantity made up of many components hence the dimensionality 
of the input space may be large. 
The purpose of signal conditioning, or "preprocessing" is to 
provide data preparation and handling, to provide geometric and radio- 
metric correction, to provide a convenient input format, to provide 
invariance, to provide in many cases a reduction in the dimensionality 
of the input data, and most importantlv, to emphasize or enhance 
aspects of the input signal which are deemed important. 
An almost universal approach to pattern recognition is to extract 
properties or features from the original signal, and to perform the 
recognition on the feature profile of the input signal. This serves 
several functions. First, by reducing the input pattern to its 
essential features, the memory required for storing the signatures is 
reduced. Secondly, by reducing the input pattern to independent 
features, a considerable amount of invariance to exact form is obtained. 
Finally, a degree of invariance to noise and background may be achieved. 
Most decision mechanisms are based on multivariate discriminate 
analysis that partitions measurement space on the basis of the training 
set signatures which then allows a decision to be made for an appropriate 
classification for each input pixel. 
The function of display is attendant with each and every one of 
the other functions of a multispectral processing system as one facet 
of the man-machine interaction. Interactive controls and commands allow 
the intervention of the operator to direct that certain things be done 
which would not otherwise be done automatically. 
Post-processing of the classification output to provide summaries 
or to integrate the results with other data bases of the user is an 
important and sometimes unconsidered function. Automatic map-like 
displays of the results are the normal approach, but may be of little 
use to some users. 
The following sections describe the techniques for implementing 
these five functions of the multispectral recognition mechanism in 
greater detail. 
3.2. Processing Techniques 
Data processing refers to the procedures, algorithms, and compu- 
tations which are applied to the raw sensor output data to transform 
it into useful information to the user. Data processing includes 
the combination of (1) data formatting, handling, editing, digitizing, 
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