Preprocessing remotely sensed data for efficient analysis and classification
Patrick Kelly, James M. White · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
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.