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Wednesday, February 5, 2020 | History

7 edition of Techniques and applications of hyperspectral image analysis found in the catalog.

Techniques and applications of hyperspectral image analysis

Techniques and applications of hyperspectral image analysis

  • 136 Want to read
  • 5 Currently reading

Published by J. Wiley in Chichester, West Sussex, England, Hoboken, NJ .
Written in English

    Subjects:
  • Image processing -- Statistical methods,
  • Multivariate analysis,
  • Multispectral photography

  • Edition Notes

    Includes bibliographical references.

    Statement[edited by] Hans Grahn and Paul Geladi.
    ContributionsGrahn, Hans., Geladi, Paul.
    Classifications
    LC ClassificationsTA1637 .T42 2007
    The Physical Object
    Paginationp. cm.
    ID Numbers
    Open LibraryOL17944637M
    ISBN 109780470010860
    LC Control Number2007021097

    It is originally developed to select informative wavelengths from contiguous spectral data, specifically applied for the first time to NIR spectroscopy. Part of the Food Engineering Series book series FSES Abstract Hyperspectral image processing extracts, stores and manipulates both spatial and spectral information contained in hyperspectral images across the visible and near-infrared portion of the electromagnetic spectrum. Smoothing improves the vision of the original spectra in addition to remove the useless information. Therefore the book should provide the engineer and technologist working in research, development, and operations in the food and agricultural industry with critical, comprehensive and readily accessible information on the art and science of hyperspectral imaging technology. This book is written by international peers who have academic and professional credentials, with each chapter addressing a particular topic or specific application of the technology. PHI Learning Pvt.

    Lorente, D. Partial Least Squares Analysis. Based on different smoothing fit methods, smoothing could be divided into moving average smoothing, Gaussian filter smoothing, median filter smoothing and Savitzky-Golay smoothing S-G smoothing [ 3334 ]. Since a spectrum is an important diagnostic, having a spectrum for each pixel allows more science cases to be addressed.

    The acquisition and processing of hyperspectral images is also referred to as imaging spectroscopy or, with reference to the hyperspectral cube, as 3D spectroscopy. He is author and coauthor of numerous papers and book chapters. Neural Netw. Line-scan systems are also used to scan materials moving by on a conveyor belt.


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Techniques and applications of hyperspectral image analysis book

Rasti, B. In this book chapter, recent advances in hyperspectral image processing algorithms and workflows for hyperspectral image processing are discussed. Hyperspectral Imaging Analysis and Applications for Food Quality explores the theoretical and practical issues associated with the development, analysis, and application of essential image processing algorithms in order to exploit hyperspectral imaging for food quality evaluations.

This book is written by international peers who have academic and professional credentials, with each chapter addressing a particular topic or specific application of the technology. Calibration models Multivariate regression techniques quantitative analysis aim to establish a relationship between the observed response values and spectral matrix.

Hyperspectral imaging is particularly useful in military surveillance because of countermeasures that military entities now take to avoid airborne surveillance. Gualtieri, J. Recent work includes the PhD dissertations of Werff [31] and Noomen. Nonetheless, there is the advantage of being able to pick and choose spectral bands, and having a direct representation of the two spatial dimensions of the scene.

Some recent standoff measurements performed allowed the evaluation of the air quality but not many remote independent methods allow for low uncertainty measurements.

A 2— CrossRef Google Scholar Hyperspectral imaging is related to multispectral imaging. Kerekes, J. Performance and reliability cookies These cookies allow us to monitor OverDrive's performance and reliability.

Akgun, T.

Techniques and Applications of Hyperspectral Image Analysis

Fast computers, sensitive detectors, and large data storage capacities are needed for analyzing hyperspectral data. Inthe first study relating this problem with hyperspectral imaging was published.

In other words, the appropriate smoothing algorithm should be selected flexibly according to the noise situation contained in the actual image. Deng, Y. This technology is continually becoming more available to the public. Each spectrum can be calibrated based on this assumption. The book is divided into three parts, which begins with an outline of the fundamentals of the technology, followed by full covering of the application in the most researched areas of meats, fruits, vegetables, grains and other foods, which mostly covers food safety and quality as well as remote sensing applicable for crop production.

Part of the Studies in Computational Intelligence book series SCI, volume Abstract Hyperspectral remote sensing has received considerable interest in recent years for a variety of industrial applications including urban mapping, precision agriculture, environmental monitoring, and military surveillance as well as computer vision applications.

Liang, H. Support Vector Machines. Loncan, L. The objective of centering is to ensure that all results will be interpretable in term of variation around the mean [ 32 ]. Amolins, K.Hyperspectral imaging, like other spectral imaging, collects and processes information from across the electromagnetic hildebrandsguld.com goal of hyperspectral imaging is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes.

You can write a book review and share your experiences. Other readers will always be interested in your opinion of the books you've read. Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them.

Hyperspectral Data Processing: Algorithm Design and Analysis is a culmination of the research conducted in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County.

Specifically, it treats hyperspectral image processing and hyperspectral signal processing as separate subjects in two different hildebrandsguld.com by: A typical hyperspectral image processing workflow for near-sensing applications includes normalization, correction, dimensionality reduction, spectral library building, and data processing.

In this book chapter, recent advances in hyperspectral image processing algorithms and workflows for hyperspectral image processing are hildebrandsguld.com by: 4. The imaging spectrometer system provides a powerful combination of spectroscopy and image processing for use in many applications in research, control and quality assessment, where spectral.

Techniques and Applications of Hyperspectral Image Analysis Hans F. Grahn and Paul Geladi {•CENTENNIAL BICENTENNIAL John Wiley & Sons, Ltd '.