
Hyperspectral Data Compression
Springer (Publisher)
Published on 17. November 2005
Book
Hardback
XII, 418 pages
978-0-387-28579-5 (ISBN)
Description
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.
Reviews / Votes
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)
More details
Edition
2006 ed.
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Research
Product notice
sewn/stitched
Paper over boards
Illustrations
XII, 418 p.
Dimensions
Height: 245 mm
Width: 167 mm
Thickness: 30 mm
Weight
833 gr
ISBN-13
978-0-387-28579-5 (9780387285795)
DOI
10.1007/0-387-28600-4
Schweitzer Classification
Other editions
Additional editions

Giovanni Motta | Francesco Rizzo | James A. Storer
Hyperspectral Data Compression
Book
10/2010
Springer
€160.49
Shipment within 15-20 days

Giovanni Motta | Francesco Rizzo | James A. Storer
Hyperspectral Data Compression
E-Book
06/2006
1st Edition
Springer
€149.79
Available for download
Persons
James A. Storer is Chair of the IEEE Data Compression Conference.
Content
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.