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.

An excellent technical reference for both academic and industrial researchers in the fields of computer science and engineering A compilation of the most current results in the field of compression of remote sensing 3D data with chapters contributed by leading researchers in the area The only book currently on the market which focuses on the newest areas of research: multispectral and hyperspectral imagery Includes supplementary material: sn.pub/extras

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

Klappentext
HYPERSPECTRAL DATA COMPRESSION presents the most recent results in the field of compression of remote sensing 3D data, with a focus on multispectral and hyperspectral imagery. This book is essential for researchers working across related fields including: multi-dimensional data compression, multispectral and hyperspectral data archives, remote sensing, scientific image processing, military and aerospace image processing, image segmentation, image classification, and target detection.

Zusammenfassung
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.

Inhalt
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.