Publication Date
9-1997
Abstract
Remotely sensed images from new generation satellites present an opportunity for scientists to investigate problems in environmental and earth science which have been previously intractable. The magnitude of data that will arise from these hyperspectral instruments create the need for innovative techniques to accomplish data reduction. This paper presents an algorithm which shows promise as a tool for reducing the dimensionality of data resulting from remote sensing. The optimality criteria for the algorithm is the Bayes Risk in the reduced dimension space.
Comments
Technical Report: UTEP-CS-97-22
In: Gary J. Erickson, Joshua T. Rychert, and C. Ray Smith (eds.), Maximum Entropy and Bayesian Methods, Kluwer, Dordrecht, 1998, pp. 271-276.