Hyperspectral Data Compression
ISBN 10: 0387285792 ISBN 13: 9780387285795
Verlag: Springer, 2005
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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.
Über die Autorin bzw. den Autor: James A. Storer is Chair of the IEEE Data Compression Conference.
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Titel: Hyperspectral Data Compression
Verlag: Springer
Erscheinungsdatum: 2005
Einband: Hardcover
Zustand: New
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Hyperspectral Data Compression
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Gebunden. Zustand: New. An excellent technical reference for both academic and industrial researchers in the fields of computer science and engineeringA compilation of the most current results in the field of compression of remote sensing 3D data with chapters contr. Artikel-Nr. 5909723
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Hyperspectral Data Compression
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Buch. Zustand: Neu. 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. Artikel-Nr. 9780387285795
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