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Book synopsis

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.HYPERSPECTRAL DATA COMPRESSION presents the most recent results in the field of compression of remote sensing 3D data, with a focus on multispectral and hyperspectral imagery. This book is essential for researchers working across related fields including: multi-dimensional data compression, multispectral and hyperspectral data archives, remote sensing, scientific image processing, military and aerospace image processing, image segmentation, image classification, and target detection.

Book details

Book category Books in English Computing & information technology Computer science Artificial intelligence

• Full title: Hyperspectral Data Compression
• Author: Giovanni Motta, Francesco Rizzo, James A. Storer
• Language: English
• Binding: Paperback
• Number of pages: 418
• EAN: 9781441939432
• ISBN: 1441939431
• ID: 01422485
• Publisher: Springer-Verlag New York Inc.
• Weight: 642 g
• Dimensions: 235 × 155 × 21 mm
• Date of publishing: 29. October 2010

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