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Proceedings Paper

Preprocessing remotely sensed data for efficient analysis and classification
Author(s): Patrick M. Kelly; James M. White
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Paper Abstract

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.

Paper Details

Date Published: 23 March 1993
PDF: 7 pages
Proc. SPIE 1963, Applications of Artificial Intelligence 1993: Knowledge-Based Systems in Aerospace and Industry, (23 March 1993); doi: 10.1117/12.141745
Show Author Affiliations
Patrick M. Kelly, Los Alamos National Lab. (United States)
James M. White, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 1963:
Applications of Artificial Intelligence 1993: Knowledge-Based Systems in Aerospace and Industry
Usama M. Fayyad; Ramasamy Uthurusamy, Editor(s)

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