Hyperspectral Data Compression

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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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.

• 3D
• 3D wavelet-based image compression
• Hyperspectral Image Data Compression
• Hyperspectral Imagery
• JPEG
• Lossless Compression
• Near-Lossless Compression
• data compression

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)

• Brandeis University, USA

  Giovanni Motta, Francesco Rizzo, James A. Storer

• Bitfone Corporation, USA

  Giovanni Motta

• University of Salerno, Italy

  Francesco Rizzo

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

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