Mapping land cover from multi or hyper spectral imagery can
proceed only on the premise that the target cover types are
spectrally different from one another. Classification is a
process of grouping pixels of similar spectral properties into
spectral classes in which each class is assumed to or designed
to correspond to a surface cover type. A number of factors can
influence the spectral "separability" of target features. They
may be observed to be spectrally different in ground
observations and yet not distinguished from one another in the
imagery because The 2 meter ground resolution distance of this
data set is sufficiently small in comparison with the size
wetland land cover features that the problem of spectral
signature mixing should not preclude the identification of most
land cover types. Training targets in which ten or more pixels
are sufficiently set back from the perimeter of the target to be
considered "pure" should not be difficult to obtain in this high
spatial resolution data. However, some li