Hyperspectral Data Compression

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Synopsis

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.

Book details

Edition:
2006
Author:
Giovanni Motta, Francesco Rizzo, James A. Storer
ISBN:
9780387286006
Related ISBNs:
9780387285795
Publisher:
Springer US
Pages:
N/A
Reading age:
Not specified
Includes images:
No
Date of addition:
2021-02-07
Usage restrictions:
Copyright
Copyright date:
2006
Copyright by:
N/A 
Adult content:
No
Language:
English
Categories:
Art and Architecture, Computers and Internet, Nonfiction