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OPTICAL SATELLITE DATA COMPRESSION AND IMPLEMENTATION
Título:
OPTICAL SATELLITE DATA COMPRESSION AND IMPLEMENTATION
Subtítulo:
Autor:
QIAN, S
Editorial:
SPIE PRESS
Año de edición:
2013
Materia
COMUNICACIONES DE DATOS
ISBN:
978-0-8194-9787-1
Páginas:
416
121,00 €

 

Sinopsis

This book provides a global review of optical satellite image and data compression theories, algorithms, and system implementations. Consisting of nine chapters, it describes a variety of lossless and near-lossless data-compression techniques and three international satellite-data-compression standards. The author shares his firsthand experience and research results in developing novel satellite-data-compression techniques for both onboard and on-ground use, user assessments of the impact that data compression has on satellite data applications, building hardware compression systems, and optimizing and deploying systems. Written with both postgraduate students and advanced professionals in mind, this handbook addresses important issues of satellite data compression and implementation, and it presents an end-to-end treatment of data compression technology.



Preface
List of Terms and Acronyms
1 Needs for Data Compression and Image Quality Metrics
1.1 Needs for Satellite Data Compression
1.2 Quality Metrics of Satellite Images
1.3 Full-Reference Metrics
1.3.1 Conventional full-reference metrics
1.3.2 Perceived-visual-quality-based full-reference metrics
1.4 Reduced-Reference Metrics
1.4.1 Four RR metrics for spatial-resolution-enhanced images
1.4.2 RR metric using the wavelet-domain natural-image statistic model
1.5 No-Reference Metrics
1.5.1 Statistic-based methods
1.5.2 NR metric for compressed images using JPEG
1.5.3 NR metric for pan-sharpened multispectral image
References
2 Lossless Satellite Data Compression
2.1 Introduction
2.2 Review of Lossless Satellite Data Compression
2.2.1 Prediction-based methods
2.2.2 Transform-based methods
2.3 Entropy Encoders
2.3.1 Adaptive arithmetic coding
2.3.2 Golomb coding
2.3.3 Exponential-Golomb coding
2.3.4 Golomb power-of-two coding
2.4 Predictors for Hyperspectral Datacubes
2.4.1 1D nearest-neighboring predictor
2.4.2 2D/3D predictors
2.4.3 Predictors within a focal plane image
2.4.4 Adaptive selection of predictor
2.4.5 Experimental results of the predictors
2.5 Lookup-Table-Based Prediction Methods
2.5.1 Single-lookup-table prediction
2.5.2 Locally averaged, interband-scaling LUT prediction
2.5.3 Quantized-index LUT prediction
2.5.4 Multiband LUT prediction
2.6 Vector-Quantization-Based Prediction Methods
2.6.1 Linear prediction
2.6.2 Grouping based on bit-length
2.6.3 Vector quantization with precomputed codebooks
2.6.4 Optimal bit allocation
2.6.5 Entropy coding
2.7 Band Reordering
2.8 Transform-Based Lossless Compression Using the KLT and DCT
2.9 Wavelet-Transform-Based Methods
2.9.1 Wavelet decomposition structure
2.9.2 Lossy-to-lossless compression: 3D set-partitioning embedded block
2.9.3 Lossy-to-lossless compression: 3D embedded zeroblock coding
References
3 International Standards for Spacecraft Data Compression
3.1 CCSDS and Three Data Compression Standards
3.2 Lossless Data Compression Standard
3.2.1 Preprocessor
3.2.2 Adaptive entropy encoder
3.2.3 Performance evaluation
3.3 Image Data Compression Standard
3.3.1 Features of the standard
3.3.2 IDC compressor
3.3.3 Selection of compression options and parameters
3.3.4 Performance evaluation
3.4 Lossless Multispectral/Hyperspectral Compression Standard
3.4.1 Compressor composition
3.4.2 Adaptive linear predictor
3.4.3 Encoder
3.4.4 Performance evaluation
References
4 Vector Quantization Data Compression
4.1 Concept of Vector Quantization Compression
4.2 Review of Conventional Fast Vector Quantization Algorithms
4.3 Fast Vector-Quantization Algorithm Based on Improved Distance to MDP
4.3.1 Analysis of the generalized Lloyd algorithm for fast training
4.3.2 Fast training based on improved distance to MDP
4.3.3 Experimental results
4.3.4 Assessment of preservation of spectral information
4.4 Fast Vector Quantization Based on Searching Nearest Partition Sets
4.4.1 Nearest partition sets
4.4.2 Upper-triangle matrix of distances
4.4.3 p-least sorting
4.4.4 Determination of NPS sizes
4.4.5 Two fast VQ search algorithms based on NPSs
4.4.6 Experimental results
4.4.7 Comparison with published fast search methods
4.5 3D VQ Compression Using Spectral-Feature-Based Binary Code
4.5.1 Spectral-feature-based binary coding
4.5.2 Fast 3D VQ using the SFBBC
4.5.3 Experimental results of the SFBBC-based VQ compression algorithm
4.6 Correlation Vector Quantization
4.6.1 Process of CVQ
4.6.2 Performance of CVQ
4.7 Training a New Codebook for a Dataset to Be Compressed
4.8 Multiple-Subcodebook Algorithm Using Spectral Index
4.8.1 Spectral indices and scene segmentation
4.8.2 Methodology of MSCA
4.8.3 Improvement in processing time
4.8.4 Experimental results of the MSCA
4.8.5 MSCA with training set subsampling
4.8.6 MSCA with training set subsampling plus SFBBC codebook training
4.8.7 MSCA with training set subsampling plus SFBBC for both codebook training and coding
4.9 Successive Approximation Multistage Vector Quantization
4.9.1 Compression procedure
4.9.2 Features
4.9.3 Test results
4.10 Hierarchical Self-Organizing Cluster Vector Quantization
4.10.1 Compression procedure
4.10.2 Features
References
5 Onboard Near-Lossless Data Compression Techniques
5.1 Near-Lossless Satellite Data Compression
5.2 Cluster SAMVQ
5.2.1 Organizing continuous data flow into regional datacubes
5.2.2 Solution for overcoming the blocking effect
5.2.3 Removing the boundary between adjacent regions
5.2.4 Attaining a fully redundant regional datacube for preventing data loss in the downlink channel
5.2.5 Compression performance comparison between SAMVQ and cluster SAMVQ
5.3 Recursive HSOCVQ
5.3.1 Reuse of codevectors of the previous region to attain a seamless conjunction between regions
5.3.2 Training codevectors for a current frame and applying them to subsequent frames
5.3.3 Two schemes of carrying forward reused codevectors trained in the previous region
5.3.4 Compression performance comparison between baseline and recursive HSOCVQ
5.4 Evaluation of Near-Lossless Performance of SAMVQ and HSOCVQ
5.4.1 Evaluation method and test dataset
5.4.2 Evaluation of a single spectrum
5.4.3 Evaluation of an entire datacube
5.5 Evaluation of SAMVQ with Regard to the Development of International Standards of Spacecraft Data Compression
5.5.1 CCSDS test datasets
5.5.2 Test results of hyperspectral datasets
5.5.3 Compression of multispectral datasets using SAMVQ
References
6 Optimizing the Performance of Onboard Data Compression
6.1 Introduction
6.2 The Effect of Raw Data Anomalies on Compression Performance
6.2.1 Anomalies in the raw hyperspectral data
6.2.2 Effect of spikes on compression performance
6.2.3 Effect of saturation