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Proceedings Paper

Impact of lossy compression on the classification of remotely sensed imagery data
Author(s): John A. Saghri; Andrew G. Tescher; Belal Mohammad
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Paper Abstract

The impact of lossy compression on the classification of the remotely-sensed imagery data is examined. The impact of compression is assessed for both types of classifications, i.e., classification via thematic map for small-footprint imagery, and classification via spectral unmixing for large- footprint imagery data. An overview of viable classification and spectral unmixing procedures are given. The criteria for measuring the impact of compression are defined. It was shown the impact of compression is insignificant for compressions ratios of less than 10. It is argued that the effective impact of compression is reduced due to the presence of others sources of inaccuracies in the original data and its relevant prediction models.

Paper Details

Date Published: 28 December 2000
PDF: 9 pages
Proc. SPIE 4115, Applications of Digital Image Processing XXIII, (28 December 2000); doi: 10.1117/12.411537
Show Author Affiliations
John A. Saghri, California Polytechnic Univ. (United States)
Andrew G. Tescher, Lockheed Martin Mission Systems (United States)
Belal Mohammad, Kuwait Univ. (Kuwait)

Published in SPIE Proceedings Vol. 4115:
Applications of Digital Image Processing XXIII
Andrew G. Tescher, Editor(s)

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