Impact of lossy compression on the classification of remotely sensed imagery data

John A. Saghri; Andrew G. Tescher; Belal Mohammad

doi:10.1117/12.411537

28 December 2000 Impact of lossy compression on the classification of remotely sensed imagery data

John A. Saghri, Andrew G. Tescher, Belal Mohammad

Author Affiliations +

Proceedings Volume 4115, Applications of Digital Image Processing XXIII; (2000) https://doi.org/10.1117/12.411537
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

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.

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John A. Saghri, Andrew G. Tescher, and Belal Mohammad "Impact of lossy compression on the classification of remotely sensed imagery data", Proc. SPIE 4115, Applications of Digital Image Processing XXIII, (28 December 2000); https://doi.org/10.1117/12.411537

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