data base of true and false targets. Filtered images are correlated

and cross correlated to determine the most discriminating frequencies.

In practice, this process is too time consuming. Certain assumption

are reasonable in spatial filtering. It is reasonable to assume that

the reference and test images do not have much more detail than is

absolutely necessary to distinguish the true target. To reduce the

number of points needed in the digital imagery, the original sampling

process was accomplished by limiting the spatial frequencies to those

required to recognize the target. Thus, the appropriate filter to

eliminate unnecessary frequency components will have the form of a

high-pass filter. The nature of this high-pass filter is dependent on

the application of the matched filter.

 The matched filter is created for a specific target. If the

target is present, the correlation is larger than for areas of the

image where the target is absent. If the target changes slightly from

the reference stored on the filter, the correlation drops. In a

practical application, small changes in the expected target are the

rule rather than the exception. If the target grows in size, rotates,

or changes its appearance slightly, the correlation may drop below the

threshold. This topic will be discussed further in Chapter V, but it

is necessary to point out that the invariance of the filter to small

changes in the target depends heavily on the frequencies used in the

correlation. Using the previous example, recall that the high-pass

images showing the edges allowed discrimination between the square and

circle. If the square were rotated slightly, the results would

change. The cross-correlation between a square and a slightly rotated

square depends on the frequencies used in the correlation. If only