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Satellite Data Compression

  • Book
  • © 2011

Overview

Editors:
  1. Bormin Huang

    1. , Space Science and Engineering Center, University of Wisconsin - Madison, Madison, USA

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  • The first book on satellite data compression
  • Presents case studies on recent advances in satellite data compression techniques via various prediction-based, lookup-table-based, transform-based, clustering-based, and projection-based approaches
  • Covers onboard compression methodology and hardware developments in several space agencies
  • Includes supplementary material: sn.pub/extras

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-ix
    Download chapter PDF

  2. Development of On-Board Data Compression Technology at Canadian Space Agency

    • Shen-En Qian
    Pages 1-28

  3. CNES Studies for On-Board Compression of High-Resolution Satellite Images

    • Carole Thiebaut, Roberto Camarero
    Pages 29-46

  4. Low-Complexity Approaches for Lossless and Near-Lossless Hyperspectral Image Compression

    • Andrea Abrardo, Mauro Barni, Andrea Bertoli, Raoul Grimoldi, Enrico Magli, Raffaele Vitulli
    Pages 47-65

  5. FPGA Design of Listless SPIHT for Onboard Image Compression

    • Yunsong Li, Juan Song, Chengke Wu, Kai Liu, Jie Lei, Keyan Wang
    Pages 67-85

  6. Outlier-Resilient Entropy Coding

    • Jordi Portell, Alberto G. Villafranca, Enrique García-Berro
    Pages 87-113

  7. Quality Issues for Compression of Hyperspectral Imagery Through Spectrally Adaptive DPCM

    • Bruno Aiazzi, Luciano Alparone, Stefano Baronti
    Pages 115-147

  8. Ultraspectral Sounder Data Compression by the Prediction-Based Lower Triangular Transform

    • Shih-Chieh Wei, Bormin Huang
    Pages 149-168

  9. Lookup-Table Based Hyperspectral Data Compression

    • Jarno Mielikainen
    Pages 169-184

  10. Multiplierless Reversible Integer TDLT/KLT for Lossy-to-Lossless Hyperspectral Image Compression

    • Jiaji Wu, Lei Wang, Yong Fang, L. C. Jiao
    Pages 185-213

  11. Divide-and-Conquer Decorrelation for Hyperspectral Data Compression

    • Ian Blanes, Joan Serra-Sagristà, Peter Schelkens
    Pages 215-232

  12. Hyperspectral Image Compression Using Segmented Principal Component Analysis

    • Wei Zhu, Qian Du, James E. Fowler
    Pages 233-251

  13. Fast Precomputed Vector Quantization with Optimal Bit Allocation for Lossless Compression of Ultraspectral Sounder Data

    • Bormin Huang
    Pages 253-267

  14. Effects of Lossy Compression on Hyperspectral Classification

    • Chulhee Lee, Sangwook Lee, Jonghwa Lee
    Pages 269-285

  15. Projection Pursuit-Based Dimensionality Reduction for Hyperspectral Analysis

    • Haleh Safavi, Chein-I Chang, Antonio J. Plaza
    Pages 287-309
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Keywords

About this book

Satellite Data Compression covers recent progress in compression techniques for multispectral, hyperspectral and ultra spectral data. A survey of recent advances in the fields of satellite communications, remote sensing and geographical information systems is included. Satellite Data Compression, contributed by leaders in this field, is the first book available on satellite data compression. It covers onboard compression methodology and hardware developments in several space agencies. Case studies are presented on recent advances in satellite data compression techniques via various prediction-based, lookup-table-based, transform-based, clustering-based, and projection-based approaches. This book provides valuable information on state-of-the-art satellite data compression technologies for professionals and students who are interested in this topic. Satellite Data Compression is designed for a professional audience comprised of computer scientists working in satellite communications, sensor system design, remote sensing, data receiving, airborne imaging and geographical information systems (GIS). Advanced-level students and academic researchers will also benefit from this book.

Editors and Affiliations

  • , Space Science and Engineering Center, University of Wisconsin - Madison, Madison, USA

    Bormin Huang

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