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Proceedings Paper

Transform coding of multispectral imagery
Author(s): John A. Saghri; Andrew G. Tescher; John T. Reagan
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Paper Abstract

Future land remote sensing satellite systems will likely be constrained in terms of downlink communication bandwidth. To alleviate this limitation the data must be compressed. In this article we present a robust and implementable compression algorithm for multispectral imagery with a selectable quality level within the near-lossless to visually lossy range. The three-dimensional terrain-adaptive transform-based algorithm involves a one-dimensional Karhunen-Loeve transform (KLT) followed by two-dimensional discrete cosine transform (DCT). The images are spectrally decorrelated via the KLT to produce the eigen images. The resulting spectrally-decorrelated eigen images are then compressed using the JPEG algorithm. The key feature of this approach is that it incorporates the best methods available to fully exploit the spectral and spatial correlation in the data. The novelty of this technique lies in its unique capability to adaptively vary the characteristics of the spectral decorrelation transformation based upon variations in the local terrain. The spectral and spatial modularity of the algorithm architecture allows the JPEG to be replaced by a totally different coder (e.g., DPCM). However, the significant practical advantage of this approach is that it is leveraged on the standard and highly developed JPEG compression technology. The algorithm is conveniently parameterized to accommodate reconstructed image fidelities ranging from near- lossless at about 5:1 compression ratio (CR) to visually lossy beginning at around 40:1 CR.

Paper Details

Date Published: 21 September 1994
PDF: 17 pages
Proc. SPIE 2298, Applications of Digital Image Processing XVII, (21 September 1994); doi: 10.1117/12.186564
Show Author Affiliations
John A. Saghri, Lockheed Palo Alto Research Lab. (United States)
Andrew G. Tescher, Lockheed Palo Alto Research Lab. (United States)
John T. Reagan, Lockheed Palo Alto Research Lab. (United States)


Published in SPIE Proceedings Vol. 2298:
Applications of Digital Image Processing XVII
Andrew G. Tescher, Editor(s)

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