Satellite Data Compression

Bormin Huang

ISBN 10: 1461411823 / ISBN 13: 9781461411826

Editore: Springer New York, Springer New York Nov 2011, 2011

Riguardo questo articolo

Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 320 pp. Englisch.

Codice articolo 9781461411826

Dati bibliografici

• Titolo: Satellite Data Compression
• Editore: Springer New York, Springer New York Nov 2011
• Data di pubblicazione: 2011
• Lingua: Inglese
• ISBN 10: 1461411823
• ISBN 13: 9781461411826
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 241 L x 160 A x 21 P (millimetri) 241 millimetri di larghezza per 160 millimetri di altezza per 21 millimetri di profondità
• Peso: 646 grammi

Descrizione articolo

Riassunto

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.

Contenuti

Development of on-board data compression technology at Canadian Space Agency.- CNES Studies for On-Board Compression of High-Resolution Satellite Images.- Low-Complexity Approaches for Lossless and Near-Lossless Hyperspectral Image Compression.- FPGA Design of Listless SPIHT for Onboard Image Compression.- Outlier-Resilient Entropy Coding.- Quality Issues for Compression of Hyperspectral Imagery Through Spectrally Adaptive DPCM.- Ultraspectral Sounder Data Compression by the Prediction-Based Lower Triangular Transform.- Lookup-Table Based Hyperspectral Data Compression.- Multiplierless Reversible Integer TDLT/KLT for Lossy-to-lossless Hyperspectral Image Compression.- Divide-and-conquer decorrelation for hyperspectral data compression.- Hyperspectral Image Compression Using Segmented Principal Component Analysis.- Fast Precomputed Vector Quantization with Optimal Bit Allocation for Lossless Compression of Ultraspectral Sounder Data.- Effects of Lossy Compression on Hyperspectral Classification.