Resolving Spectral Mixtures

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

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  • Author : Anonim
  • Publisher : Elsevier
  • Pages : 674 pages
  • ISBN : 0444636447
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKResolving Spectral Mixtures

Resolving Spectral Mixtures

Resolving Spectral Mixtures
  • Author : Anonim
  • Publisher : Elsevier
  • Release : 13 August 2016
GET THIS BOOKResolving Spectral Mixtures

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

An Image Fusion Algorithm for Spatially Enhancing Spectral Mixture Maps

An Image Fusion Algorithm for Spatially Enhancing Spectral Mixture Maps
  • Author : Harry N. Gross
  • Publisher : Unknown Publisher
  • Release : 01 January 1997
GET THIS BOOKAn Image Fusion Algorithm for Spatially Enhancing Spectral Mixture Maps

An image fusion algorithm, based upon spectral mixture analysis, is presented. The algorithm combines low spatial resolution multi/hyperspectral data with high spatial resolution sharpening image(s) to create high resolution material maps. Spectral (un)mixing estimates the percentage of each material (called endmembers) within each low resolution pixel. The outputs of unmixing are endmember fraction images (material maps) at the spatial resolution of the multispectral system. This research includes developing an improved unmixing algorithm based upon stepwise regression. In

Hyperspectral Imaging

Hyperspectral Imaging
  • Author : Anonim
  • Publisher : Elsevier
  • Release : 29 September 2019
GET THIS BOOKHyperspectral Imaging

Hyperspectral Imaging, Volume 32, presents a comprehensive exploration of the different analytical methodologies applied on hyperspectral imaging and a state-of-the-art analysis of applications in different scientific and industrial areas. This book presents, for the first time, a comprehensive collection of the main multivariate algorithms used for hyperspectral image analysis in different fields of application. The benefits, drawbacks and suitability of each are fully discussed, along with examples of their application. Users will find state-of-the art information on the machinery for hyperspectral

Spectral Mixture for Remote Sensing

Spectral Mixture for Remote Sensing
  • Author : Yosio Edemir Shimabukuro,Flávio Jorge Ponzoni
  • Publisher : Springer
  • Release : 10 November 2018
GET THIS BOOKSpectral Mixture for Remote Sensing

This book explains in a didactic way the basic concepts of spectral mixing, digital numbers and orbital sensors, and then presents the linear modelling technique of spectral mixing and the generation of fractional images. In addition to presenting a theoretical basis for spectral mixing, the book provides examples of practical applications such as projects for estimating and monitoring deforested areas in the Amazon. In its seven chapters, the book offers remote sensing techniques to understand the main concepts, methods, and

Evaluation of Two Applications of Spectral Mixing Models to Image Fusion

Evaluation of Two Applications of Spectral Mixing Models to Image Fusion
  • Author : Gary D. Robinson
  • Publisher : Unknown Publisher
  • Release : 01 August 1997
GET THIS BOOKEvaluation of Two Applications of Spectral Mixing Models to Image Fusion

Many applications in remote sensing require merging low-resolution multispectral or hyperspectral images with high-resolution panchromatic images to create high-resolution multispectral or hyperspectral material maps. A number of methods are currently in use to produce such hybrid imagery. Until now, these methods have only been evaluated independently, and have not been compared to one another to determine an optimum method. This research performed a quantitative test of three image fusion procedures. The first method involves first sharpening low-resolution multispectral data using

Handbook of Blind Source Separation

Handbook of Blind Source Separation
  • Author : Pierre Comon,Christian Jutten
  • Publisher : Academic Press
  • Release : 17 February 2010
GET THIS BOOKHandbook of Blind Source Separation

Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Going beyond a machine learning perspective, the book reflects recent results in signal processing and numerical analysis, and includes topics such