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Hyperspectral data compression

Format Post in Programming BY Francesco Rizzo, Giovanni Motta, James A. Storer

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Hyperspectral data compression Francesco Rizzo, Giovanni Motta, James A. Storer is available to download <table><tr><td colspan="2"><strong style="font-size:1.This material is available do download at niSearch.com on Francesco Rizzo, Giovanni Motta, James A. Storer's eBooks, 2em;">Hyperspectral data compression</strong><br/>Francesco Rizzo, Giovanni Motta, James A.Hyperspectral data compression Textbook Storer</td></tr> <tr> <td><b>Type:</b></td> <td>eBook</td> </tr> <tr> <td><b>Released:</b></td> <td>2010</td> </tr> <tr> <td><b>Publisher:</b></td> <td>Springer</td> </tr> <tr> <td><b>Page Count:</b></td> <td>421</td> </tr> <tr> <td><b>Format:</b></td> <td>pdf</td> </tr> <tr> <td><b>Language:</b></td> <td>English</td> </tr> <tr> <td><b>ISBN-10:</b></td> <td>1441939431</td> </tr> <tr> <td><b>ISBN-13:</b></td> <td>9781441939432</td> </tr> </table> 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.

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