Interpreting remotely-sensed data typically requires expensive, specialized computing machinery capable of storing and manipulating large amounts of data quickly. In this paper, we present a method for accurately analyzing and categorizing remotely-sensed data on much smaller, less-expensive platforms. Data size is reduced in such a way as to retain the integrity of the original data, where the format of the resultant data set lends itself well to providing an efficient, interactive method of data classification.
P.M. Kelly and J.M. White. Preprocessing remotely-sensed data for efficient analysis and classification. In SPIE Vol. 1963 Applications of Artificial Intelligence 1993: Knowledge-Based Systems in Aerospace and Industry, pages 24-30, 1993. Los Alamos National Laboratory Technical Report LA-UR-93-0301. [ Abstract | PDF (202 KB) ]
