Hyperspectral Data Compression (Hardcover, 2006 ed.)

Giovanni Motta, Francesco Rizzo, James A. Storer

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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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Release date:	November 2005
First published:	2006
Editors:	Giovanni Motta • Francesco Rizzo • James A. Storer
Dimensions:	235 x 156 x 23mm (L x W x T)
Format:	Hardcover
Pages:	418
Edition:	2006 ed.
ISBN-13:	978-0-387-28579-5
Categories:	Books > Earth & environment > Geography > Cartography, geodesy & geographic information systems (GIS) > Geographical information systems (GIS) Books > Earth & environment > Geography > Cartography, geodesy & geographic information systems (GIS) > Remote sensing Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics > General Promotions
LSN:	0-387-28579-2
Barcode:	9780387285795