Resolving Spectral Mixtures With Applications from Ultrafast Time-Resolved Spectroscopy to Super-Resolution Imaging

✍ Scribed by Cyril Ruckebusch (Eds.)

• Publisher: Elsevier
• Year: 2016
• Tongue: English
• Leaves: 649
• Series: Data Handling in Science and Technology 30
• Edition: 1
• Category: Library

✦ Synopsis

Resolving Spectral Mixtures: With Applications from Ultrafast Time-Resolved Spectroscopy to Superresolution Imaging offers a comprehensive look into the most important models and frameworks essential to resolving the spectral unmixing problem-from multivariate curve resolution and multi-way analysis to Bayesian positive source separation and nonlinear unmixing. Unravelling total spectral data into the contributions from individual unknown components with limited prior information is a complex problem that has attracted continuous interest for almost four decades.

Spectral unmixing is a topic of interest in statistics, chemometrics, signal processing, and image analysis. For decades, researchers from these fields were often unaware of the work in other disciplines due to their different scientific and technical backgrounds and interest in different objects or samples. This led to the development of quite different approaches to solving the same problem. This multi-authored book will bridge the gap between disciplines with contributions from a number of well-known and strongly active chemometric and signal processing research groups.

Among chemists, multivariate curve resolution methods are preferred to extract information about the nature, amount, and location in time (process) and space (imaging and microscopy) of chemical constituents in complex samples. In signal processing, assumptions are usually around statistical independence of the extracted components. However, the chapters include the complexity of the spectral data to be unmixed as well as dimensionality and size of the data sets. Advanced spectroscopy is the key thread linking the different chapters. Applications cover a large part of the electromagnetic spectrum. Time-resolution ranges from femtosecond to second in process spectroscopy and spatial resolution covers the submicronic to macroscopic scale in hyperspectral imaging.

• Demonstrates how and why data analysis, signal processing, and chemometrics are essential to the spectral unmixing problem
• Guides the reader through the fundamentals and details of the different methods
• Presents extensive plots, graphical representations, and illustrations to help readers understand the features of different techniques and to interpret results
• Bridges the gap between disciplines with contributions from a number of well-known and highly active chemometric and signal processing research groups

✦ Table of Contents

Content:
Copyright Page iv
Contributors Pages xv-xvii
Preface Pages xix-xx Cyril Ruckebusch
Foreword Pages xxi-xxiv Marcel Maeder
Chapter 1 - Introduction Pages 1-4 C. Ruckebusch
Chapter 2 - Multivariate Curve Resolution-Alternating Least Squares for Spectroscopic Data Pages 5-51 A. de Juan, R. Tauler
Chapter 3 - Spectral Unmixing Using the Concept of Pure Variables Pages 53-99 S. Kucheryavskiy, W. Windig, A. Bogomolov
Chapter 4 - Ambiguities in Multivariate Curve Resolution Pages 101-133 A. Malik, R. Tauler
Chapter 5 - On the Analysis and Computation of the Area of Feasible Solutions for Two-, Three-, and Four-Component Systems Pages 135-184 M. Sawall, A. Jürß, H. Schröder, K. Neymeyr
Chapter 6 - Linear and Nonlinear Unmixing in Hyperspectral Imaging Pages 185-224 N. Dobigeon, Y. Altmann, N. Brun, S. Moussaoui
Chapter 7 - Independent Components Analysis: Theory and Applications Pages 225-277 D. Jouan-Rimbaud Bouveresse, D.N. Rutledge
Chapter 8 - Bayesian Positive Source Separation for Spectral Mixture Analysis Pages 279-309 D. Brie, S. Moussaoui, S. Miron, C. Carteret, M. Dossot
Chapter 9 - Multivariate Curve Resolution of Wavelet Compressed Data Pages 311-332 P. de B. Harrington
Chapter 10 - Chemometric Resolution of Complex Higher Order Chromatographic Data with Spectral Detection Pages 333-352 D.K. Pinkerton, K.M. Pierce, R.E. Synovec
Chapter 11 - Multivariate Curve Resolution of (Ultra)Fast Photoinduced Process Spectroscopy Data Pages 353-379 O. Devos, S. Aloïse, M. Sliwa, R. Métivier, J.-P. Placial, C. Ruckebusch
Chapter 12 - Experimental and Data Analytical Approaches to Automating Multivariate Curve Resolution in the Analysis of Hyperspectral Images Pages 381-408 D.M. Haaland, H.D.T. Jones, J.A. Timlin
Chapter 13 - Multiresolution Analysis and Chemometrics for Pattern Enhancement and Resolution in Spectral Signals and Images Pages 409-451 M. Li Vigni, M. Cocchi
Chapter 14 - A Smoothness Constraint in Multivariate Curve Resolution-Alternating Least Squares of Spectroscopy Data Pages 453-476 S. Hugelier, O. Devos, C. Ruckebusch
Chapter 15 - Super-Resolution in Vibrational Spectroscopy: From Multiple Low-Resolution Images to High-Resolution Images Pages 477-517 M. Offroy, L. Duponchel
Chapter 16 - Multivariate Curve Resolution for Magnetic Resonance Image Analysis: Applications in Prostate Cancer Biomarkers Development Pages 519-550 J.M. Prats-Montalbán, E. Aguado Sarrió, A. Ferrer
Chapter 17 - Endmember Library Approaches to Resolve Spectral Mixing Problems in Remotely Sensed Data: Potential, Challenges, and Applications Pages 551-577 B. Somers, L. Tits, D. Roberts, E. Wetherley
Chapter 18 - Spectral–Spatial Unmixing Approaches in Hyperspectral VNIR/SWIR Imaging Pages 579-611 N. Gorretta, C. Gomez
Chapter 19 - Sparse-Based Modeling of Hyperspectral Data Pages 613-634 R. Calvini, A. Ulrici, J.M. Amigo
Index Pages 635-649

✦ Subjects

Spectral imaging;Spectrum analysis;SCIENCE;Chemistry;Analytic