Hyperspectral data compression

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.