Hyperspectral Data Compression

1. Auflage. Softcover version of original hardcover Auflage 2006
ISBN: 978-1-4419-3943-2
Verlag: Springer

Buch, Englisch, 418 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 651 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Motta, Giovanni

Rizzo, Francesco

Storer, James A.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Grafikprogrammierung
• Geowissenschaften Geologie Geodäsie, Kartographie, Fernerkundung
• Geowissenschaften Geographie | Raumplanung Geodäsie, Kartographie, GIS, Fernerkundung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenkompression, Dokumentaustauschformate
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Spiele-Programmierung, Rendering, Animation
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Angewandte Optik

Inhaltsverzeichnis

An Architecture for the Compression of Hyperspectral Imagery.- Lossless Predictive Compression of Hyperspectral Images.- Lossless Hyperspectral Image Compression via Linear Prediction.- Lossless Compression of Ultraspectral Sounder Data.- Locally Optimal Partitioned Vector Quantization of Hyperspectral Data.- Near-Lossless Compression of Hyperspectral Imagery Through Crisp/Fuzzy Adaptive DPCM.- Joint Classification and Compression of Hyperspectral Images.- Predictive Coding of Hyperspectral Images.- Coding of Hyperspectral Imagery with Trellis-Coded Quantization.- Three-Dimensional Wavelet-Based Compression of Hyperspectral Images.- Spectral/Spatial Hyperspectral Image Compression.- Compression of Earth Science Data with JPEG2000.- Spectral Ringing Artifacts in Hyperspectral Image Data Compression.

Über Autor(innen)

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