Hyperspectral Data Compression

Giovanni Motta

ISBN 10: 0387285792 / ISBN 13: 9780387285795

Edité par Springer Us Nov 2005, 2005

A propos de cet article

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.

N° de réf. du vendeur 9780387285795

Détails bibliographiques

• Titre: Hyperspectral Data Compression
• Éditeur: Springer Us Nov 2005
• Date de parution: 2005
• Langue: anglais
• ISBN à 10 chiffres: 0387285792
• ISBN à 13 chiffres: 9780387285795
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 245 L x 167 H x 30 P (en millimètres) 245 millimètres de largeur par 167 millimètres de hauteur par 30 millimètres de profondeur
• Poids: 833 grammes

A propos de ce titre

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.

À propos de l?auteur

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