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| hyperspectral image processing matlab code: Hyperspectral Data Processing Chein-I Chang, 2013-04-08 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 categories. Most materials covered in this book can be used in conjunction with the author’s first book, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, without much overlap. Many results in this book are either new or have not been explored, presented, or published in the public domain. These include various aspects of endmember extraction, unsupervised linear spectral mixture analysis, hyperspectral information compression, hyperspectral signal coding and characterization, as well as applications to conceal target detection, multispectral imaging, and magnetic resonance imaging. Hyperspectral Data Processing contains eight major sections: Part I: provides fundamentals of hyperspectral data processing Part II: offers various algorithm designs for endmember extraction Part III: derives theory for supervised linear spectral mixture analysis Part IV: designs unsupervised methods for hyperspectral image analysis Part V: explores new concepts on hyperspectral information compression Parts VI & VII: develops techniques for hyperspectral signal coding and characterization Part VIII: presents applications in multispectral imaging and magnetic resonance imaging Hyperspectral Data Processing compiles an algorithm compendium with MATLAB codes in an appendix to help readers implement many important algorithms developed in this book and write their own program codes without relying on software packages. Hyperspectral Data Processing is a valuable reference for those who have been involved with hyperspectral imaging and its techniques, as well those who are new to the subject. |
| hyperspectral image processing matlab code: Advances in Hyperspectral Image Processing Techniques Chein-I Chang, 2022-11-22 Advances in Hyperspectral Image Processing Techniques Authoritative and comprehensive resource covering recent hyperspectral imaging techniques from theory to applications Advances in Hyperspectral Image Processing Techniques is derived from recent developments of hyperspectral imaging (HSI) techniques along with new applications in the field, covering many new ideas that have been explored and have led to various new directions in the past few years. The work gathers an array of disparate research into one resource and explores its numerous applications across a wide variety of disciplinary areas. In particular, it includes an introductory chapter on fundamentals of HSI and a chapter on extensive use of HSI techniques in satellite on-orbit and on-board processing to aid readers involved in these specific fields. The book’s content is based on the expertise of invited scholars and is categorized into six parts. Part I provides general theory. Part II presents various Band Selection techniques for Hyperspectral Images. Part III reviews recent developments on Compressive Sensing for Hyperspectral Imaging. Part IV includes Fusion of Hyperspectral Images. Part V covers Hyperspectral Data Unmixing. Part VI offers different views on Hyperspectral Image Classification. Specific sample topics covered in Advances in Hyperspectral Image Processing Techniques include: Two fundamental principles of hyperspectral imaging Constrained band selection for hyperspectral imaging and class information-based band selection for hyperspectral image classification Restricted entropy and spectrum properties for hyperspectral imaging and endmember finding in compressively sensed band domain Hyperspectral and LIDAR data fusion, fusion of band selection methods for hyperspectral imaging, and fusion using multi-dimensional information Advances in spectral unmixing of hyperspectral data and fully constrained least squares linear spectral mixture analysis Sparse representation-based hyperspectral image classification; collaborative hyperspectral image classification; class-feature weighted hyperspectral image classification; target detection approach to hyperspectral image classification With many applications beyond traditional remote sensing, ranging from defense and intelligence, to agriculture, to forestry, to environmental monitoring, to food safety and inspection, to medical imaging, Advances in Hyperspectral Image Processing Techniques is an essential resource on the topic for industry professionals, researchers, academics, and graduate students working in the field. |
| hyperspectral image processing matlab code: Hyperspectral Data Processing Chein-I Chang, 2013-02-01 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 categories. Most materials covered in this book can be used in conjunction with the author’s first book, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, without much overlap. Many results in this book are either new or have not been explored, presented, or published in the public domain. These include various aspects of endmember extraction, unsupervised linear spectral mixture analysis, hyperspectral information compression, hyperspectral signal coding and characterization, as well as applications to conceal target detection, multispectral imaging, and magnetic resonance imaging. Hyperspectral Data Processing contains eight major sections: Part I: provides fundamentals of hyperspectral data processing Part II: offers various algorithm designs for endmember extraction Part III: derives theory for supervised linear spectral mixture analysis Part IV: designs unsupervised methods for hyperspectral image analysis Part V: explores new concepts on hyperspectral information compression Parts VI & VII: develops techniques for hyperspectral signal coding and characterization Part VIII: presents applications in multispectral imaging and magnetic resonance imaging Hyperspectral Data Processing compiles an algorithm compendium with MATLAB codes in an appendix to help readers implement many important algorithms developed in this book and write their own program codes without relying on software packages. Hyperspectral Data Processing is a valuable reference for those who have been involved with hyperspectral imaging and its techniques, as well those who are new to the subject. |
| hyperspectral image processing matlab code: Processing of Hyperspectral Medical Images Robert Koprowski, 2016-12-01 This book presents new methods of analyzing and processing hyperspectral medical images, which can be used in diagnostics, for example for dermatological images. The algorithms proposed are fully automatic and the results obtained are fully reproducible. Their operation was tested on a set of several thousands of hyperspectral images and they were implemented in Matlab. The presented source code can be used without licensing restrictions. This is a valuable resource for computer scientists, bioengineers, doctoral students, and dermatologists interested in contemporary analysis methods. |
| hyperspectral image processing matlab code: Hyperspectral Image Analysis Saurabh Prasad, Jocelyn Chanussot, 2020-04-27 This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful. |
| hyperspectral image processing matlab code: Techniques and Applications of Hyperspectral Image Analysis Hans Grahn, Paul Geladi, 2007-09-27 Techniques and Applications of Hyperspectral Image Analysis 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. |
| hyperspectral image processing matlab code: Image Processing for Remote Sensing C.H. Chen, 2007-10-17 Edited by leaders in the field, with contributions by a panel of experts, Image Processing for Remote Sensing explores new and unconventional mathematics methods. The coverage includes the physics and mathematical algorithms of SAR images, a comprehensive treatment of MRF-based remote sensing image classification, statistical approaches for |
| hyperspectral image processing matlab code: Hyperspectral Image Analysis Saurabh Prasad, Jocelyn Chanussot, 2020-06-09 This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful. |
| hyperspectral image processing matlab code: Hyperspectral Remote Sensing Marcus Borengasser, William S. Hungate, Russell Watkins, 2007-12-13 Land management issues, such as mapping tree species, recognizing invasive plants, and identifying key geologic features, require an understanding of complex technical issues before the best decisions can be made. Hyperspectral remote sensing is one the technologies that can help with reliable detection and identification. Presenting the fundamenta |
| hyperspectral image processing matlab code: Processing and Analysis of Hyperspectral Data Jie Chen, Yingying Song, Hengchao Li, 2020-01-22 Hyperspectral imagery has received considerable attention in the last decade as it provides rich spectral information and allows the analysis of objects that are unidentifiable by traditional imaging techniques. It has a wide range of applications, including remote sensing, industry sorting, food analysis, biomedical imaging, etc. However, in contrast to RGB images from which information can be intuitively extracted, hyperspectral data is only useful with proper processing and analysis. This book covers theoretical advances of hyperspectral image processing and applications of hyperspectral processing, including unmixing, classification, super-resolution, and quality estimation with classical and deep learning methods. |
| hyperspectral image processing matlab code: Handbook of Research on Science Education and University Outreach as a Tool for Regional Development Narasimharao, B. Pandu Ranga, Wright, Elizabeth, Prasad, Shashidhara, Joshi, Meghana, 2017-02-01 Higher education institutions play a vital role in their surrounding communities. Besides providing a space for enhanced learning opportunities, universities can utilize their resources for social and economic interests. The Handbook of Research on Science Education and University Outreach as a Tool for Regional Development is a comprehensive reference source for the latest scholarly material on the expanded role of universities for community engagement initiatives. Providing in-depth coverage across a range of topics, such as resource sharing, educational administration, and technological applications, this handbook is ideally designed for educators, graduate students, professionals, academics, and practitioners interested in the active involvement of education institutions in community outreach. |
| hyperspectral image processing matlab code: Sensors in Agriculture Dimitrios Moshou, 2019-06-12 Agriculture requires technical solutions for increasing production while lessening environmental impact by reducing the application of agro-chemicals and increasing the use of environmentally friendly management practices. A benefit of this is the reduction of production costs. Sensor technologies produce tools to achieve the abovementioned goals. The explosive technological advances and developments in recent years have enormously facilitated the attainment of these objectives, removing many barriers for their implementation, including the reservations expressed by farmers. Precision agriculture and ‘smart farming’ are emerging areas where sensor-based technologies play an important role. Farmers, researchers, and technical manufacturers are joining their efforts to find efficient solutions, improvements in production, and reductions in costs. This book brings together recent research and developments concerning novel sensors and their applications in agriculture. Sensors in agriculture are based on the requirements of farmers, according to the farming operations that need to be addressed. |
| hyperspectral image processing matlab code: Application of Spectroscopy in Agricultural Environment and Livestock Breeding Leizi Jiao, Jinchang Ren, Kun Wang, 2023-08-29 Intelligent agriculture is the inevitable trend of future agriculture. As the brain of intelligent agriculture, advanced sensors determine the degree of agricultural environment and crop perception. However, an outstanding problem is the long-standing lack of effective sensing and monitoring tools in agriculture. Most information still relies on time-consuming and complex laboratory analysis and can only be off-line measured. Therefore, in-situ, on-line and sensitive methods for monitoring information about the agricultural environment and crops are urgently needed. With the development of material science, manufacturing technology and spectroscopy, more and more fast and high-precision spectroscopic methods and sensors are applied in the agricultural environment, livestock breeding and crop. Especially with the development of spectral enhancement, image processing and deep learning techniques, the study of the advanced spectroscopic methods to rapidly and precisely detect heavy metals and nutrients in the soil, harmful gas, water pollutant and crop stress will reduce the probability of subjective error judgment and improves agricultural management and production efficiency. Application of spectroscopy in an agricultural environment and livestock breeding is a research topic of Frontiers in Physics that publishes original research on the field of agricultural physics. And the aim of this research topic is to explore novel and groundbreaking spectroscopic methods to rapidly and in-situ detect information about the agricultural environment, livestock breeding and crop stress, which could be beneficial in the development of Intelligent agriculture. |
| hyperspectral image processing matlab code: The Image Processing Handbook John C. Russ, F. Brent Neal, 2018-09-03 Consistently rated as the best overall introduction to computer-based image processing, The Image Processing Handbook covers two-dimensional (2D) and three-dimensional (3D) imaging techniques, image printing and storage methods, image processing algorithms, image and feature measurement, quantitative image measurement analysis, and more. Incorporating image processing and analysis examples at all scales, from nano- to astro-, this Seventh Edition: Features a greater range of computationally intensive algorithms than previous versions Provides better organization, more quantitative results, and new material on recent developments Includes completely rewritten chapters on 3D imaging and a thoroughly revamped chapter on statistical analysis Contains more than 1700 references to theory, methods, and applications in a wide variety of disciplines Presents 500+ entirely new figures and images, with more than two-thirds appearing in color The Image Processing Handbook, Seventh Edition delivers an accessible and up-to-date treatment of image processing, offering broad coverage and comparison of algorithms, approaches, and outcomes. |
| hyperspectral image processing matlab code: Image Analysis Puneet Sharma, Filippo Maria Bianchi, 2017-05-22 The two-volume set LNCS 10269 and 10270 constitutes the refereed proceedings of the 20th Scandinavian Conference on Image Analysis, SCIA 2017, held in Tromsø, Norway, in June 2017. The 87 revised papers presented were carefully reviewed and selected from 133 submissions. The contributions are structured in topical sections on history of SCIA; motion analysis and 3D vision; pattern detection and recognition; machine learning; image processing and applications; feature extraction and segmentation; remote sensing; medical and biomedical image analysis; faces, gestures and multispectral analysis. |
| hyperspectral image processing matlab code: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery , 2007 |
| hyperspectral image processing matlab code: Optical Imaging for Biomedical and Clinical Applications Ahmad Fadzil Mohamad Hani, Dileep Kumar, 2017-10-30 Optical imaging is a rapidly emerging imaging technique that has been successfjully translated into biomedical applications ranging from clinical diagnosis to molecular biology. This book includes an introductory section to explore various optical imaging devices and their functionality and roles for biomedical applications such as dermatology and ophthalmology. Recent developments as exemplified with the authors research are explored in detail. In depth discussion of other disease conditions and their diagnosis with optical imaging techniques are also covered. |
| hyperspectral image processing matlab code: Speech, Audio, Image and Biomedical Signal Processing using Neural Networks Bhanu Prasad, S.R.M. Prasanna, 2008-01-03 Humans are remarkable in processing speech, audio, image and some biomedical signals. Artificial neural networks are proved to be successful in performing several cognitive, industrial and scientific tasks. This peer reviewed book presents some recent advances and surveys on the applications of artificial neural networks in the areas of speech, audio, image and biomedical signal processing. It chapters are prepared by some reputed researchers and practitioners around the globe. |
| hyperspectral image processing matlab code: Advances in Porous Semiconductor Research Thierry Djenizian, Nicolas Hans Voelcker, 2020-03-30 |
| hyperspectral image processing matlab code: ASC MSRC Wright Cycles Journal Fall 2003 , |
| hyperspectral image processing matlab code: Computer Vision: Concepts, Methodologies, Tools, and Applications Management Association, Information Resources, 2018-02-02 The fields of computer vision and image processing are constantly evolving as new research and applications in these areas emerge. Staying abreast of the most up-to-date developments in this field is necessary in order to promote further research and apply these developments in real-world settings. Computer Vision: Concepts, Methodologies, Tools, and Applications is an innovative reference source for the latest academic material on development of computers for gaining understanding about videos and digital images. Highlighting a range of topics, such as computational models, machine learning, and image processing, this multi-volume book is ideally designed for academicians, technology professionals, students, and researchers interested in uncovering the latest innovations in the field. |
| hyperspectral image processing matlab code: Comprehensive Chemometrics , 2009-03-09 Designed to serve as the first point of reference on the subject, Comprehensive Chemometrics presents an integrated summary of the present state of chemical and biochemical data analysis and manipulation. The work covers all major areas ranging from statistics to data acquisition, analysis, and applications. This major reference work provides broad-ranging, validated summaries of the major topics in chemometrics—with chapter introductions and advanced reviews for each area. The level of material is appropriate for graduate students as well as active researchers seeking a ready reference on obtaining and analyzing scientific data. Features the contributions of leading experts from 21 countries, under the guidance of the Editors-in-Chief and a team of specialist Section Editors: L. Buydens; D. Coomans; P. Van Espen; A. De Juan; J.H. Kalivas; B.K. Lavine; R. Leardi; R. Phan-Tan-Luu; L.A. Sarabia; and J. Trygg Examines the merits and limitations of each technique through practical examples and extensive visuals: 368 tables and more than 1,300 illustrations (750 in full color) Integrates coverage of chemical and biological methods, allowing readers to consider and test a range of techniques Consists of 2,200 pages and more than 90 review articles, making it the most comprehensive work of its kind Offers print and online purchase options, the latter of which delivers flexibility, accessibility, and usability through the search tools and other productivity-enhancing features of ScienceDirect |
| hyperspectral image processing matlab code: Laser Spectroscopy and Laser Imaging Helmut H. Telle, Ángel González Ureña, 2018-04-17 a very valuable book for graduate students and researchers in the field of Laser Spectroscopy, which I can fully recommend —Wolfgang Demtröder, Kaiserslautern University of Technology How would it be possible to provide a coherent picture of this field given all the techniques available today? The authors have taken on this daunting task in this impressive, groundbreaking text. Readers will benefit from the broad overview of basic concepts, focusing on practical scientific and real-life applications of laser spectroscopic analysis and imaging. Chapters follow a consistent structure, beginning with a succinct summary of key principles and concepts, followed by an overview of applications, advantages and pitfalls, and finally a brief discussion of seminal advances and current developments. The examples used in this text span physics and chemistry to environmental science, biology, and medicine. Focuses on practical use in the laboratory and real-world applications Covers the basic concepts, common experimental setups Highlights advantages and caveats of the techniques Concludes each chapter with a snapshot of cutting-edge advances This book is appropriate for anyone in the physical sciences, biology, or medicine looking for an introduction to laser spectroscopic and imaging methodologies. Helmut H. Telle is a full professor at the Instituto Pluridisciplinar, Universidad Complutense de Madrid, Spain. Ángel González Ureña is head of the Department of Molecular Beams and Lasers, Instituto Pluridisciplinar, Universidad Complutense de Madrid, Spain. |
| hyperspectral image processing matlab code: Hyperspectral Imaging for Food Quality Analysis and Control Da-Wen Sun, 2010-06-29 Based on the integration of computer vision and spectrscopy techniques, hyperspectral imaging is a novel technology for obtaining both spatial and spectral information on a product. Used for nearly 20 years in the aerospace and military industries, more recently hyperspectral imaging has emerged and matured into one of the most powerful and rapidly growing methods of non-destructive food quality analysis and control. Hyperspectral Imaging for Food Quality Analysis and Control provides the core information about how this proven science can be practically applied for food quality assessment, including information on the equipment available and selection of the most appropriate of those instruments. Additionally, real-world food-industry-based examples are included, giving the reader important insights into the actual application of the science in evaluating food products. - Presentation of principles and instruments provides core understanding of how this science performs, as well as guideline on selecting the most appropriate equipment for implementation - Includes real-world, practical application to demonstrate the viability and challenges of working with this technology - Provides necessary information for making correct determination on use of hyperspectral imaging |
| hyperspectral image processing matlab code: Geometric Structure of High-Dimensional Data and Dimensionality Reduction Jianzhong Wang, 2012-04-28 Geometric Structure of High-Dimensional Data and Dimensionality Reduction adopts data geometry as a framework to address various methods of dimensionality reduction. In addition to the introduction to well-known linear methods, the book moreover stresses the recently developed nonlinear methods and introduces the applications of dimensionality reduction in many areas, such as face recognition, image segmentation, data classification, data visualization, and hyperspectral imagery data analysis. Numerous tables and graphs are included to illustrate the ideas, effects, and shortcomings of the methods. MATLAB code of all dimensionality reduction algorithms is provided to aid the readers with the implementations on computers. The book will be useful for mathematicians, statisticians, computer scientists, and data analysts. It is also a valuable handbook for other practitioners who have a basic background in mathematics, statistics and/or computer algorithms, like internet search engine designers, physicists, geologists, electronic engineers, and economists. Jianzhong Wang is a Professor of Mathematics at Sam Houston State University, U.S.A. |
| hyperspectral image processing matlab code: Sensing and Artificial Intelligence Solutions for Food Manufacturing Daniel Hefft, Charles Oluwaseun Adetunji, 2023-03-31 This book gives readers a practical introduction into machine learning and sensing techniques, their design and ultimately specific applications that could improve food production. It shows how these sensing and computing systems are suitable for process implementation in food factories. This book starts by giving the reader an overview of the historic structures of food manufacturing standards and how they defined today’s manufacturing. It is followed by a topical introduction for professionals in the food industries in topics such as AI, machine learning, and neural networks. It also includes an explanation of the different sensor systems and their basic principles. It shows how these sensing and computing systems are suitable for process implementation in food factories and what types of sensing systems have already been proven to deliver benefit to the food manufacturing industries. The authors also discuss issues around food safety, labelling, and traceability and how sensing and AI can help to resolve issues. They also use case studies and specific examples that can show the benefit of such technologies compared to current approaches. This book is a practical introduction and handbook for students, food engineers, technologists and process engineers on the benefits and challenges around modern manufacturing systems following Industry 4.0 approaches. |
| hyperspectral image processing matlab code: High Performance Computing in Remote Sensing Antonio J. Plaza, Chein-I Chang, 2007-10-18 Solutions for Time-Critical Remote Sensing Applications The recent use of latest-generation sensors in airborne and satellite platforms is producing a nearly continual stream of high-dimensional data, which, in turn, is creating new processing challenges. To address the computational requirements of time-critical applications, researchers |
| hyperspectral image processing matlab code: Satellite Image Analysis: Clustering and Classification Surekha Borra, Rohit Thanki, Nilanjan Dey, 2019-02-08 Thanks to recent advances in sensors, communication and satellite technology, data storage, processing and networking capabilities, satellite image acquisition and mining are now on the rise. In turn, satellite images play a vital role in providing essential geographical information. Highly accurate automatic classification and decision support systems can facilitate the efforts of data analysts, reduce human error, and allow the rapid and rigorous analysis of land use and land cover information. Integrating Machine Learning (ML) technology with the human visual psychometric can help meet geologists’ demands for more efficient and higher-quality classification in real time. This book introduces readers to key concepts, methods and models for satellite image analysis; highlights state-of-the-art classification and clustering techniques; discusses recent developments and remaining challenges; and addresses various applications, making it a valuable asset for engineers, data analysts and researchers in the fields of geographic information systems and remote sensing engineering. |
| hyperspectral image processing matlab code: Biomedical Spectroscopy , 2000 |
| hyperspectral image processing matlab code: Hyperspectral Imaging Analysis and Applications for Food Quality N.C. Basantia, Leo M.L. Nollet, Mohammed Kamruzzaman, 2018-11-16 In processing food, hyperspectral imaging, combined with intelligent software, enables digital sorters (or optical sorters) to identify and remove defects and foreign material that are invisible to traditional camera and laser sorters. 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. It outlines strategies and essential image processing routines that are necessary for making the appropriate decision during detection, classification, identification, quantification, and/or prediction processes. Features Covers practical issues associated with the development, analysis, and application of essential image processing for food quality applications Surveys the breadth of different image processing approaches adopted over the years in attempting to implement hyperspectral imaging for food quality monitoring Explains the working principles of hyperspectral systems as well as the basic concept and structure of hyperspectral data Describes the different approaches used during image acquisition, data collection, and visualization The book is divided into three sections. Section I discusses the fundamentals of Imaging Systems: How can hyperspectral image cube acquisition be optimized? Also, two chapters deal with image segmentation, data extraction, and treatment. Seven chapters comprise Section II, which deals with Chemometrics. One explains the fundamentals of multivariate analysis and techniques while in six other chapters the reader will find information on and applications of a number of chemometric techniques: principal component analysis, partial least squares analysis, linear discriminant model, support vector machines, decision trees, and artificial neural networks. In the last section, Applications, numerous examples are given of applications of hyperspectral imaging systems in fish, meat, fruits, vegetables, medicinal herbs, dairy products, beverages, and food additives. |
| hyperspectral image processing matlab code: Comprehensive Remote Sensing Shunlin Liang, 2017-11-08 Comprehensive Remote Sensing, Nine Volume Set covers all aspects of the topic, with each volume edited by well-known scientists and contributed to by frontier researchers. It is a comprehensive resource that will benefit both students and researchers who want to further their understanding in this discipline. The field of remote sensing has quadrupled in size in the past two decades, and increasingly draws in individuals working in a diverse set of disciplines ranging from geographers, oceanographers, and meteorologists, to physicists and computer scientists. Researchers from a variety of backgrounds are now accessing remote sensing data, creating an urgent need for a one-stop reference work that can comprehensively document the development of remote sensing, from the basic principles, modeling and practical algorithms, to various applications. Fully comprehensive coverage of this rapidly growing discipline, giving readers a detailed overview of all aspects of Remote Sensing principles and applications Contains ‘Layered content’, with each article beginning with the basics and then moving on to more complex concepts Ideal for advanced undergraduates and academic researchers Includes case studies that illustrate the practical application of remote sensing principles, further enhancing understanding |
| hyperspectral image processing matlab code: Comprehensive Chemometrics Steven Brown, Roma Tauler, Beata Walczak, 2020-05-26 Comprehensive Chemometrics, Second Edition, Four Volume Set features expanded and updated coverage, along with new content that covers advances in the field since the previous edition published in 2009. Subject of note include updates in the fields of multidimensional and megavariate data analysis, omics data analysis, big chemical and biochemical data analysis, data fusion and sparse methods. The book follows a similar structure to the previous edition, using the same section titles to frame articles. Many chapters from the previous edition are updated, but there are also many new chapters on the latest developments. Presents integrated reviews of each chemical and biological method, examining their merits and limitations through practical examples and extensive visuals Bridges a gap in knowledge, covering developments in the field since the first edition published in 2009 Meticulously organized, with articles split into 4 sections and 12 sub-sections on key topics to allow students, researchers and professionals to find relevant information quickly and easily Written by academics and practitioners from various fields and regions to ensure that the knowledge within is easily understood and applicable to a large audience Presents integrated reviews of each chemical and biological method, examining their merits and limitations through practical examples and extensive visuals Bridges a gap in knowledge, covering developments in the field since the first edition published in 2009 Meticulously organized, with articles split into 4 sections and 12 sub-sections on key topics to allow students, researchers and professionals to find relevant information quickly and easily Written by academics and practitioners from various fields and regions to ensure that the knowledge within is easily understood and applicable to a large audience |
| hyperspectral image processing matlab code: Hyperspectral Imaging Technology in Food and Agriculture Bosoon Park, Renfu Lu, 2015-09-29 Hyperspectral imaging or imaging spectroscopy is a novel technology for acquiring and analysing an image of a real scene by computers and other devices in order to obtain quantitative information for quality evaluation and process control. Image processing and analysis is the core technique in computer vision. With the continuous development in hardware and software for image processing and analysis, the application of hyperspectral imaging has been extended to the safety and quality evaluation of meat and produce. Especially in recent years, hyperspectral imaging has attracted much research and development attention, as a result rapid scientific and technological advances have increasingly taken place in food and agriculture, especially on safety and quality inspection, classification and evaluation of a wide range of food products, illustrating the great advantages of using the technology for objective, rapid, non-destructive and automated safety inspection as well as quality control. Therefore, as the first reference book in the area, Hyperspectral Imaging Technology in Food and Agriculture focuses on these recent advances. 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. Hyperspectral Imaging Technology in Food and Agriculture is written by international peers who have both academic and professional credentials, with each chapter addressing in detail one aspect of the relevant technology, thus highlighting the truly international nature of the work. 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. It should also serve as an essential reference source to undergraduate and postgraduate students and researchers in universities and research institutions. |
| hyperspectral image processing matlab code: Fourier Ptychographic Imaging Guoan Zheng, 2016-06-30 This book demonstrates the concept of Fourier ptychography, a new imaging technique that bypasses the resolution limit of the employed optics. In particular, it transforms the general challenge of high-throughput, high-resolution imaging from one that is coupled to the physical limitations of the optics to one that is solvable through computation. Demonstrated in a tutorial form and providing many MATLAB® simulation examples for the reader, it also discusses the experimental implementation and recent developments of Fourier ptychography. This book will be of interest to researchers and engineers learning simulation techniques for Fourier optics and the Fourier ptychography concept. |
| hyperspectral image processing matlab code: A Side Scan Sonar Image Target Detection Algorithm Based on a Neutrosophic Set and Diffusion Maps Xiao Wang , Jianhu Zhao , Bangyan Zhu , Tingchen Jiang , Tiantian Qin , To accurately achieve side scan sonar (SSS) image target detection, a novel target detection algorithm based on a neutrosophic set (NS) and diffusion maps (DMs) is proposed in this paper. |
| hyperspectral image processing matlab code: Coastal Monitoring through Partnerships Brian D. Melzian, Virginia Engle, Malissa McAlister, Shabeg S. Sandhu, 2013-03-14 As the coastal human population increases in the United States, there will likely be increasing environmental and socioeconomic pressures on our coastal and estuarine environments. Monitoring the condition of all our nation's coastal and estuarine ecosystems over the long term is more than any one program can accomplish on its own. Therefore, it is crucial that monitoring programs at all levels (local, state, and federal) cooperate in the collection, sharing, and use of environmental data. This volume is the proceedings of the Coastal Monitoring Through Partnerships symposium that was held in Pensacola, Florida in April of 2001, and was organized by the U.S. Environmental Protection Agency's (EPA's) Environmental Monitoring and Assessment Program (EMAP), and the Council of State Governments (CSG). It contains papers that describe various multi-disciplinary coastal and estuarine environmental monitoring programs, designed and implemented by using regional and national partnerships with federal and state agencies, academia, Native American tribes, and nongovernmental organizations. In addition, it includes papers on modeling and data management; monitoring and assessment of benthic communities; development of biological indicators and interlaboratory sediment comparisons; microbiological modeling and indicators; and monitoring and assessment of phytoplankton and submerged aquatic vegetation. There are many components involved in determining the overall impacts of anthropogenic stressors on coastal and estuarine waters. It will take strong partnerships like those described in this volume to ensure that we have healthy and sustainable coastal and estuarine environments, now and in the future. |
| hyperspectral image processing matlab code: International Journal of Advanced Remote Sensing and GIS Cloud Publications, 2012-01-01 International Journal of Advanced Remote Sensing and GIS (IJARSG, ISSN 2320 – 0243) is an open-access peer-reviewed scholarly journal publishes original research papers, reviews, case study, case reports, and methodology articles in all aspects of Remote Sensing and GIS including associated fields. This Journal commits to working for quality and transparency in its publishing by following standard Publication Ethics and Policies. |
| hyperspectral image processing matlab code: Nanoinformatics Isao Tanaka, 2018-01-15 This open access book brings out the state of the art on how informatics-based tools are used and expected to be used in nanomaterials research. There has been great progress in the area in which “big-data” generated by experiments or computations are fully utilized to accelerate discovery of new materials, key factors, and design rules. Data-intensive approaches play indispensable roles in advanced materials characterization. Materials informatics is the central paradigm in the new trend. Nanoinformatics is its essential subset, which focuses on nanostructures of materials such as surfaces, interfaces, dopants, and point defects, playing a critical role in determining materials properties. There have been significant advances in experimental and computational techniques to characterize individual atoms in nanostructures and to gain quantitative information. The collaboration of researchers in materials science and information science is growing actively and is creating a new trend in materials science and engineering. |
| hyperspectral image processing matlab code: Intelligent Computing Theories and Application De-Shuang Huang, Kang-Hyun Jo, Jianqiang Li, Valeriya Gribova, Vitoantonio Bevilacqua, 2021-08-09 This two-volume set of LNCS 12836 and LNCS 12837 constitutes - in conjunction with the volume LNAI 12838 - the refereed proceedings of the 17th International Conference on Intelligent Computing, ICIC 2021, held in Shenzhen, China in August 2021. The 192 full papers of the three proceedings volumes were carefully reviewed and selected from 458 submissions. The ICIC theme unifies the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. The theme for this conference is “Advanced Intelligent Computing Methodologies and Applications.” The papers are organized in the following subsections: Evolutionary Computation and Learning, Image and signal Processing, Information Security, Neural Networks, Pattern Recognition Swarm Intelligence and Optimization, and Virtual Reality and Human-Computer Interaction. |
| hyperspectral image processing matlab code: Optical Engineering , 2003 Publishes papers reporting on research and development in optical science and engineering and the practical applications of known optical science, engineering, and technology. |
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