Hyperspectral Data Compression (Paperback, Softcover reprint of hardcover 1st ed. 2006)

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:	October 2010
First published:	2006
Editors:	Giovanni Motta • Francesco Rizzo • James A. Storer
Dimensions:	235 x 155 x 21mm (L x W x T)
Format:	Paperback
Pages:	418
Edition:	Softcover reprint of hardcover 1st ed. 2006
ISBN-13:	978-1-4419-3943-2
Categories:	Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision Books > Computing & IT > Applications of computing > Image processing > General
LSN:	1-4419-3943-1
Barcode:	9781441939432

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