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Hyperspectral Data Compression

Hyperspectral Data Compression provides a survey of recent results in the field of compression of remote sensed 3D data, with a particular interest in hyperspectral imagery. Chapter 1 addresses compression architecture, and reviews and compares compression methods. Chapters 2 through 4 focus on lossless compression (where the decompressed image must be bit for bit identical to the original). Chapter 5, contributed by the editors, describes a lossless algorithm based on vector quantization with extensions to near lossless and possibly lossy compression for efficient browning and pure pixel classification. Chapter 6 deals with near lossless compression while. Chapter 7 considers lossy techniques constrained by almost perfect classification. Chapters 8 through 12 address lossy compression of hyperspectral imagery, where there is a tradeoff between compression achieved and the quality of the decompressed image. Chapter 13 examines artifacts that can arise from lossy compression.
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Table of Contents

An Architecture for the Compression of Hyperspectral Imagery.- Lossless Predictive Compression of Hyperspectral Images.- Lossless Hyperspectral Image Compression via Linear Prediction.- Lossless Compression of Ultraspectral Sounder Data.- Locally Optimal Partitioned Vector Quantization of Hyperspectral Data.- Near-Lossless Compression of Hyperspectral Imagery Through Crisp/Fuzzy Adaptive DPCM.- Joint Classification and Compression of Hyperspectral Images.- Predictive Coding of Hyperspectral Images.- Coding of Hyperspectral Imagery with Trellis-Coded Quantization.- Three-Dimensional Wavelet-Based Compression of Hyperspectral Images.- Spectral/Spatial Hyperspectral Image Compression.- Compression of Earth Science Data with JPEG2000.- Spectral Ringing Artifacts in Hyperspectral Image Data Compression.

About the Author

James A. Storer is Chair of the IEEE Data Compression Conference.


From the reviews: "Motta, Rizzo, and Storer ... are veterans in the field of data compression, both individually and collaboratively. They bring together a concentrated set of contributed papers, focusing on compressing hyperspectral (multidimensional) data. ... This compendium describes cutting-edge compression technology, and is sure to occupy an important position in the current literature of the field. The editors have accomplished their goal of making this technology available to the educational and industrial communities." (R. Goldberg, Computing Reviews, Vol. 50 (1), January, 2009)

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