This item is printed on demand - Print on Demand Titel. 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.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 432 pp. Englisch.

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Bibliographic Details

Title: Hyperspectral Data Compression
Publisher: Springer US, Springer US Okt 2010
Publication Date: 2010
Language: English
ISBN 10: 1441939431
ISBN 13: 9781441939432
Binding: Taschenbuch
Condition: Neu
Dimensions: 235 millimeters width by 155 millimeters height by 24 millimeters depth
Weight: 651 grams

About this title

Synopsis

Interest in remote sensing applications and platforms has grown dramatically in recent years. This leading-edge reference surveys the latest results in the field of compression of remote sensing 3D data, with a focus on hyperspectral imagery. Unique in scope in this emerging research area, the book will be essential for industrial and academic researchers working in the fields of multi-dimensional data compression, remote sensing, military and aerospace image processing, homeland security, archival of large volumes of scientific and medical data, image classification, and target detection.

About the Author

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

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