Application Of Canonical Analysis To Multispectral Scanner Data

A mutispectral scanner (MSS) obtains remotely sensed data of the earth's surface in vector form, one vector element for each spectral sensing band of the MSS. Data generated by either airborne or orbiting MSS systems are organized in a lattice of scan lines and elements within scan lines. Each element, called a pixel, is represented by an observational vector and corresponds to a relatively small specific area on the ground. The total observations for a sensed scene frequently are in the millions and are composed of a number of populations, each representing a different land form category. Based on samples from the populations, the mean vectors, or spectral signatures, and the covariance matrices for the categories may be estimated. From these, an among category covariance matrix and a combined within category covariance matrix can be found. The total among category variance is the trace of the among category covariance matrix. Canonical analysis finds a linear transformation matrix such that the first transform variable accounts for the maximum possible part of the total among category variance. Similarly, the second transform variable, orthogonal to the first, accounts for the maximum possible part of the remainder of the total variance, and so on. Such transformation often results in over 95 percent of the total variance accounted for in the first three canonical variables, even though the initial number of spectral bands exceeded twenty. Transformation of bulk data into three canonical variates allows color map products to be developed by assigning each variable to a different basic color. Such color products are believed to be more useful for photo-interpretation than color products resulting from any combinations of three of the initial spectral bands.