Satellite Data Compression

__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. Front Matter....Pages i-ix Development of On-Board Data Compression Technology at Canadian Space Agency....Pages 1-28 CNES Studies for On-Board Compression of High-Resolution Satellite Images....Pages 29-46 Low-Complexity Approaches for Lossless and Near-Lossless Hyperspectral Image Compression....Pages 47-65 FPGA Design of Listless SPIHT for Onboard Image Compression....Pages 67-85 Outlier-Resilient Entropy Coding....Pages 87-113 Quality Issues for Compression of Hyperspectral Imagery Through Spectrally Adaptive DPCM....Pages 115-147 Ultraspectral Sounder Data Compression by the Prediction-Based Lower Triangular Transform....Pages 149-168 Lookup-Table Based Hyperspectral Data Compression....Pages 169-184 Multiplierless Reversible Integer TDLT/KLT for Lossy-to-Lossless Hyperspectral Image Compression....Pages 185-213 Divide-and-Conquer Decorrelation for Hyperspectral Data Compression....Pages 215-232 Hyperspectral Image Compression Using Segmented Principal Component Analysis....Pages 233-251 Fast Precomputed Vector Quantization with Optimal Bit Allocation for Lossless Compression of Ultraspectral Sounder Data....Pages 253-267 Effects of Lossy Compression on Hyperspectral Classification....Pages 269-285 Projection Pursuit-Based Dimensionality Reduction for Hyperspectral Analysis....Pages 287-309