Information extraction techniques for multi-spectral scanner data

Multispectral data recognition and information extraction problems considered are: (1) signature extension for improved recognition processing over large areas; (2) choice of density functions for recognition decision rules; (3) channel selection for cost reduction; and (4) radiation balance mapping for interpretation of wide spectrum scanner data. The formulation of a simulation model and reprocessing of both aircraft and space data reduces scan angle variations and extends signatures from one altitude to another. Comparison of the usefulness of empirical density functions and that of Gaussian density functions for recognition processing establishes the advantages of normal assumption for individual fields in processing of multispectral scanner data. Also reported is a procedure for producing radiation balance maps from wide spectra by analyzing energy budgets of vegetation and other surface materials through partitioning net absorbed radiant energy and estimating incoming power density at both short and long wavelengths.