{"product_id":"techniques-and-applications-of-hyperspectral-image-analysis-isbn-9780470010860","title":"Techniques and Applications of Hyperspectral Image Analysis","description":"\u003ci\u003eTechniques and Applications of Hyperspectral Image Analysis\u003c\/i\u003e gives an introduction to the field of image analysis using hyperspectral techniques, and includes definitions and instrument descriptions. Other imaging topics that are covered are segmentation, regression and classification. The book discusses how high quality images of large data files can be structured and archived. Imaging techniques also demand accurate calibration, and are covered in sections about multivariate calibration techniques. The book explains the most important instruments for hyperspectral imaging in more technical detail. A number of applications from medical and chemical imaging are presented and there is an emphasis on data analysis including modeling, data visualization, model testing and statistical interpretation.  Preface.  \u003cp\u003eList of Contributors.\u003c\/p\u003e \u003cp\u003eList of Abbreviations.\u003c\/p\u003e \u003cp\u003e1 Multivariate Images, Hyperspectral Imaging: Background and Equipment \u003ci\u003e(Paul L. M. Geladi, Hans F. Grahn and James E. Burger).\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2 Principles of Multivariate Image Analysis (MIA) in Remote Sensing, Technology and Industry \u003ci\u003e(Kim H. Esbensen and Thorbjørn T. Lied).\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3 Clustering and Classification in Multispectral Imaging for Quality Inspection of Postharvest Products \u003ci\u003e(Jacco C. Noordam and Willie H. A. M. van den Broek).\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4 Self-modeling Image Analysis with SIMPLISMA \u003ci\u003e(Willem Windig, Sharon Markel and Patrick M. Thompson).\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5 Multivariate Analysis of Spectral Images Composed of Count Data \u003ci\u003e(Michael R. Keenan).\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6 Hyperspectral Image Data Conditioning and Regression Analysis \u003ci\u003e(James E. Burger and Paul L. M. Geladi).\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7 Principles of Image Cross-validation (ICV): Representative Segmentation of Image Data Structures \u003ci\u003e(Kim H. Esbensen and Thorbjørn T. Lied).\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8 Detection, Classification, and Quantification in Hyperspectral Images Using Classical Least Squares Models \u003ci\u003e(Neal B. Gallagher).\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9 Calibration Standards and Image Calibration \u003ci\u003e(Paul L. M. Geladi).\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10 Multivariate Movies and their Applications in Pharmaceutical and Polymer Dissolution Studies \u003ci\u003e(Jaap van der Weerd and Sergei G. Kazarian).\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11 Multivariate Image Analysis of Magnetic Resonance Images: Component Resolution with the Direct Exponential Curve Resolution Algorithm (DECRA) \u003ci\u003e(Brian Antalek, Willem Windig and Joseph P. Hornak).\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12 Hyperspectral Imaging Techniques: an Attractive Solution for the Analysis of Biological and Agricultural Materials \u003ci\u003e(Vincent Baeten, Juan Antonio Fernández Pierna and Pierre Dardenne).\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13 Application of Multivariate Image Analysis in Nuclear Medicine: Principal Component Analysis (PCA) on Dynamic Human Brain Studies with Positron Emission Tomography (PET) for Discrimination of Areas of Disease at High Noise Levels \u003ci\u003e(Pasha Razifar and Mats Bergström).\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14 Near Infrared Chemical Imaging: Beyond the Pictures \u003ci\u003e(E. Neil Lewis, Janie Dubois, Linda H. Kidder and Kenneth S. Haber).\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e  \u003cb\u003ePaul Geladi\u003c\/b\u003e received a Ph.D. in chemistry at the University of Antwerp in 1979. In 1990, he became associate professor at Umeå University, with interests in multivariate calibration, multivariate image analysis and multiway analysis. He was awarded the EAS award for Chemometrics in 2002. Currently he is head of research for NIRCE centered in Umeå and Vasa. Paul Geladi has coauthored about 90 scientific papers and some 20 book chapters and has given many invited lectures throughout Europe and North America. He was European editor of Journal of Chemometrics from 1989 - 1995, and has been review editor of the journal since 1999. He served as a member of the Editorial Board of Chemometrics and Intelligent Laboratory Systems from 1986 to 1991.  \u003cp\u003e\u003cb\u003eHans F Grahn\u003c\/b\u003e received his Ph.D. in Physical Organic Chemistry in 1986. Following several years of work abroad he began MRI studies in the laboratory of Dr Zeverenyi at SUNY Health Center, NY. At this time (1988) Hans also began to collaborate with Paul Geladi and the MIA (Multivariate Image Analysis) software for MRI multivariate images was written. In 1990 Hans received funding from the Swedish Natural Science Foundation for 2D NMR work at Umeå University. In 1991 he began a 3 year project at AstraZeneca. During this period he continued to collaborate with the pharmaceutical industry and the Karolinska Institute, where he received a position as a preclinical researcher and associate Professor at a new MRI -centre. Hans has more than 35 coauthored scientific papers and book chapters. He is now active as Business Developer in the Medical Imaging business and is also active in his own company.\u003c\/p\u003e \u003cp\u003eThis book is about multivariate and hyperspectral imaging, not only on how to produce the images but on how to clean, transform, analyze and presnet them. The emphasis is on visualization of images, models and statistical diagnostics but some useful n umbers and equations are given where needed. The book is divided into two parts- the first chapters are about definitions, nomenclature and data analytical and visualization aspects, i.e. the definition of multivariate and hyperspectral images. They introduce nomenclature; insights into factor and component modeling used on the spectral information in the images; the concepts and models for regression modeling on hyperspectral images and multivariate image regression (MIR).\u003c\/p\u003e \u003cp\u003eThe final five applied chapters present a diverse catalog of things that can be done with hyperspectral images using different types of variables including:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e\u003cb\u003eMultivariate movies in different variables, mainly optical, infrared, Raman and nuclear magnetic resonance.\u003c\/b\u003e\u003c\/li\u003e \u003cli\u003e\u003cb\u003eThe DAECRA technique as it can be used on phantoms and brain images in magnetic resonance imaging.\u003c\/b\u003e\u003c\/li\u003e \u003cli\u003e\u003cb\u003eAgricultural and biological applications of optical multivariate and hyperspectral imaging.\u003c\/b\u003e\u003c\/li\u003e \u003cli\u003e\u003cb\u003eBrain studies using positron emission tomography (PET). PET images are extremely noisy and require special care.\u003c\/b\u003e\u003c\/li\u003e \u003cli\u003e\u003cb\u003eChemical imaging using near infrared spectroscopy. Pharmaceutical granulate mixtures are the examples used.\u003c\/b\u003e\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis book is intended for both an audience new to multivariate image analysis as well as to those who are already using image analysis techniques. It is relevant to academic and industrial researchers in chemistry, biology and medical sciences.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47990139388133,"sku":"NP9780470010860","price":226.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470010860.jpg?v=1761786655","url":"https:\/\/k12savings.com\/products\/techniques-and-applications-of-hyperspectral-image-analysis-isbn-9780470010860","provider":"K12savings","version":"1.0","type":"link"}