Hyperspectral Data Compression

Giovanni Motta

ISBN 10: 0387285792 / ISBN 13: 9780387285795

Published by Springer Us Nov 2005, 2005

About this Item

Neuware - 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.

Seller Inventory # 9780387285795

Bibliographic Details

• Title: Hyperspectral Data Compression
• Publisher: Springer Us Nov 2005
• Publication Date: 2005
• Language: English
• ISBN 10: 0387285792
• ISBN 13: 9780387285795
• Binding: Buch
• Condition: Neu
• Dimensions: 245W x 167H x 30D millimeters 245 millimeters width by 167 millimeters height by 30 millimeters depth
• Weight: 833 grams

About this title

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.

About the Author

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