THE BASIS OF EXTRACTIVE PROCESSING 
Detection and discrimination of an object by multispectral sensing 
requires differences in the radiation received from the object and its 
surroundings. This radiation "contrast” is due to differences in reflectance, 
emittance, or temperature between the object and its surroundings. 
Discrimination means devising a decision rule, based on measurements from 
a sample from each of two or more given classes, which will enable us to 
assign new samples to the correct classes when we do not know to which they 
belong. Classification means to assign samples into groups which shall be as 
distinct as possible. In discrimination the existence of the classes is 
given; in classification it is a matter to be determined. Supervised learning 
or discrimination should achieve greater efficiency because it takes advantage 
of available human knowledge and intelligence. In some cases, such human 
assistance is not possible, and an unsupervised learning approach may be 
required. The user defines which process is employed. Having made the 
distinction we use the terms interchangeably. The terms identification and 
recognition will also be employed for convenience. 
Basic to this process of discrimination is the concept of a signature. 
In general, a signature is any collection of observable features of a material 
or its condition that can be used for precise classification. The features 
that make up a signature may all be observed simultaneously or in a sequence 
of observations spread over a considerable time period. 
A basic element of spectral information extraction is the realization that 
spectral signatures cannot be completely deterministic. That is, spectral 
reflectivity and emissivity measurements of natural objects exhibit some 
dispersion around a mean value (i.e., spectral signatures are statistical in 
character). Thus as we will use the term, a spectral signature is a probability 
density function (or set of such functions) which characterize the statistical 
attributes of a finite set of observations of a material and can be used to 
classify the material or its condition to some degree of fineness. 
At the basis of discrimination theory is the necessity to realize that 
optimum discrimination techniques require not only that the procedures be 
tailored to recognize the item or material of interest, but also simultaneously, 
that they be tailored to reject other items or materials that lie in the 
vicinity of the desired materials but that are not of interest, i.e., the 
backgrounds in which the items of interest are embedded. Two types of error 
are possible: failure to classify all of the desired class actually present 
as that class and misclassification of other classes as that class. Photo 
interpreters commonly call these errors of omission and commission, 
respectively. 
Examination of measurements of the spectral reflectivity and emissivity 
of materials can aid in the development of effective discrimination procedures 
by providing insight into the basic optical properties of the materials of 
interest and their natural variability.