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Proceedings Paper

Parallel computing and data compression for pattern matching in remote sensing image databases
Author(s): Robert A. Schowengerdt; Justin D. Paola
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Paper Abstract

If scientists are to fully exploit the terabytes of remote sensing imagery in present and future libraries, techniques must be developed for efficient and reliable pattern matching. In this paper we investigate two technologies that will play major roles in this large-scale computing challenge. We describe a software neural network algorithm that can be used for pattern matching and test its performance for a multispectral classification task on a single processor workstation and a parallel processing machine, the CM-5. We also look at the impact of a commonly used data compression standard, JPEG, on the accuracy of pattern matching for spectral signatures. We find that accuracy degrades as expected as the compression ratio increases, but that the neural net algorithm is significantly more robust than the statistically based maximum-likelihood algorithm. Empirical results are presented from our experiments and discussed.

Paper Details

Date Published: 21 December 1994
PDF: 11 pages
Proc. SPIE 2318, Recent Advances in Remote Sensing and Hyperspectral Remote Sensing, (21 December 1994); doi: 10.1117/12.197240
Show Author Affiliations
Robert A. Schowengerdt, Univ. of Arizona (United States)
Justin D. Paola, Univ. of Arizona (United States)

Published in SPIE Proceedings Vol. 2318:
Recent Advances in Remote Sensing and Hyperspectral Remote Sensing
Pat S. Chavez Jr.; Carlo M. Marino; Robert A. Schowengerdt, Editor(s)

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