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Hyperspectral Data Compression

  • Book
  • © 2006

Overview

Editors:
  1. Giovanni Motta

    1. Brandeis University, USA
      Bitfone Corporation, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Francesco Rizzo

    1. Brandeis University, USA
      University of Salerno, Italy

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    You can also search for this editor in PubMed   Google Scholar

  3. James A. Storer

    1. Brandeis University, USA

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  • 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

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-xi
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  2. An Architecture for the Compression of Hyperspectral Imagery

    • Mark R. Pickering, Michael J. Ryan
    Pages 1-34

  3. Lossless Predictive Compression of Hyperspectral Images

    • Hongqiang Wang, Khalid Sayood
    Pages 35-55

  4. Lossless Hyperspectral Image Compression via Linear Prediction

    • Jarno Mielikainen, Pekka Toivanen
    Pages 57-74

  5. Lossless Compression of Ultraspectral Sounder Data

    • Bormin Huang, Alok Ahuja, Hung-Lung Huang
    Pages 75-105

  6. Locally Optimal Partitioned Vector Quantization of Hyperspectral Data

    • G. Motta, F. Rizzo, J. A. Storer
    Pages 107-146

  7. Near-Lossless Compression of Hyperspectral Imagery Through Crisp/Fuzzy Adaptive DPCM

    • Bruno Aiazzi, Luciano Alparone, Stefano Baronti, Cinzia Lastri, Leonardo Santurri
    Pages 147-177

  8. Joint Classification and Compression of Hyperspectral Images

    • Grégoire Mercier, Marc Lennon
    Pages 178-196

  9. Predictive Coding of Hyperspectral Images

    • Agnieszka C. Miguel, Richard E. Ladner, Eve A. Riskin, Scott Hauck, Dane K. Barney, ‡Amanda R. Askew et al.
    Pages 197-231

  10. Coding of Hyperspectral Imagery with Trellis-Coded Quantization

    • Glen P. Abousleman
    Pages 233-272

  11. Three-Dimensional Wavelet-Based Compression of Hyperspectral Images

    • Xiaoli Tang, William A. Pearlman
    Pages 273-308

  12. Spectral/Spatial Hyperspectral Image Compression

    • Bharath Ramakrishna, Antonio J. Plaza, Chein-I Chang, Hsuan Ren, Qian Du, Chein-Chi Chang
    Pages 309-346

  13. Compression of Earth Science Data with JPEG2000

    • Prajit Kulkarni, Ali Bilgin, Michael W. Marcellin, Joseph C. Dagher, James H. Kasner, Thomas J. Flohr et al.
    Pages 347-378

  14. Spectral Ringing Artifacts in Hyperspectral Image Data Compression

    • Matthew Klimesh, Aaron Kiely, Hua Xie, Nazeeh Aranki
    Pages 379-405

  15. Back Matter

    Pages 407-417
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Keywords

About this book

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.

Reviews

From the reviews:

"Motta, Rizzo, and Storer … are veterans in the field of data compression, both individually and collaboratively. They bring together a concentrated set of contributed papers, focusing on compressing hyperspectral (multidimensional) data. … This compendium describes cutting-edge compression technology, and is sure to occupy an important position in the current literature of the field. The editors have accomplished their goal of making this technology available to the educational and industrial communities." (R. Goldberg, Computing Reviews, Vol. 50 (1), January, 2009)

Editors and Affiliations

  • Brandeis University, USA

    Giovanni Motta, Francesco Rizzo, James A. Storer

  • Bitfone Corporation, USA

    Giovanni Motta

  • University of Salerno, Italy

    Francesco Rizzo

About the editors

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

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