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Proceedings Paper

Feature-prioritized compression of multispectral imagery data
Author(s): John A. Saghri; Andrew G. Tescher; Mohamed J. Omran
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Paper Abstract

A joint classification-compression scheme that provides the user with added capability to prioritize features of interest in the compression process is proposed. The dual compression system includes a primary unit for conventional coding of multispectral image set followed by an auxiliary unit to code the resulting error induced on pixel vectors that represent features of interest. This technique effectively allows features of interest in the scene to be coded at a relatively higher precision level than the nonessential features. Prioritized features are selected from a thematic map or directly specified by their unique spectral signatures. Using the specified spectral signatures of the prioritized features as endmembers, a modified linear spectral unmixing procedure is applied to the original data as well as the decoded data. The resulting two sets of concentration maps, which represent prioritized features before and after compression, are compared and the differences between them are coded via an auxiliary compression unit and transmitted to the receiver along with conventionally coded image set. At the receiver, the recovered differences are blended back into the decoded data for an enhanced restoration of the prioritized features. The utility of this approach is that it works with any multispectral compression scheme. This method has been applied to test imagery from various platforms including NOAA's AVHRR (1.1 km GSD), and LANDSAT 5 TM (30 m GSD), LANDSAT 5 MSS (79 m GSD).

Paper Details

Date Published: 7 December 2001
PDF: 7 pages
Proc. SPIE 4472, Applications of Digital Image Processing XXIV, (7 December 2001); doi: 10.1117/12.449767
Show Author Affiliations
John A. Saghri, California Polytechnic State Univ. (United States)
Andrew G. Tescher, Compression Science, Inc. (United States)
Mohamed J. Omran, Kuwait Univ. (Kuwait)

Published in SPIE Proceedings Vol. 4472:
Applications of Digital Image Processing XXIV
Andrew G. Tescher, Editor(s)

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