Download or read book Applications of Independent Component Analysis in Hyperspectral Data Exploitation written by Jing Wang and published by . This book was released on 2006 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Hyperspectral Data Exploitation written by Chein-I Chang and published by John Wiley & Sons. This book was released on 2007-06-11 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Authored by a panel of experts in the field, this book focuses on hyperspectral image analysis, systems, and applications. With discussion of application-based projects and case studies, this professional reference will bring you up-to-date on this pervasive technology, wether you are working in the military and defense fields, or in remote sensing technology, geoscience, or agriculture.

Download or read book Hyperspectral Data Dimensionality Reduction and Applications written by Haleh Safavi and published by . This book was released on 2013 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dimensionality Reduction (DR) is a commonly used preprocessing technique to reduce data volumes to accomplish various tasks such as data compression, communication, transmission. Unfortunately, it also comes at a price which has two challenging issues needed to be resolved. One is that it must know the number of dimensions, q needed to be retained after DR a priori. The other is how to preserve desired data information through DR process. This dissertation develops Projection Pursuit (PP)-based Progressive Dimensionality Reduction (PDR) to address these two issues. To resolve the second issue, the PP-based DR (PP-DR) extends two well-known components analysis techniques, Principal Components Analysis (PCA) and Independent Component Analysis (ICA) via a Projection Index which can be designed to capture desired data information. The resulting PP-DR is called PIPP-DR which includes PCA and ICA as it special cases with PI specified by data variance and mutual information where the PCA-generated Principal Components (PCs) and ICA-generated Independent Components (ICs) are also extended to Projection Index components (PICs). In order to deal with the first issue the PDR is particularly developed to by ranking PICs, in the sense of information prioritization measured by a specific criterion so the DR can be performed in a forward manner by dimensionality expansion or in a backward manner by dimensionality reduction progressively without appealing for the knowledge about the value of q. However, in order to reduce computational complexity a newly developed concept called Virtual Dimensionality (VD) can be used to lower bound and upper bound on the q, which are the VD and twice VD respectively. Since the value of q generally varies with different applications, the PDR is further extended to Dynamic Dimensionality Reduction (DDR) which allows users adapt the value of the q to meet various applications. To avoid repeatedly re-implementing DR as the traditional DR techniques do, the DDR makes use of PDR to adapt various applications. For illustrative purposes, three applications in hyperspectral data exploitation, land cover/use classification, Linear Spectral Mixture Analysis (LSMA) for spectral unmixing and endmember extraction are used for demonstration. Experimental results show that adapting the value of q to perform DDR is much more effective than traditional DR using a fixed value of q in all different applications.

Download or read book Assessing and Enabling Independent Component Analysis as a Hyperspectral Unmixing Approach written by Matthew R. Stites and published by . This book was released on 2012 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: As a result of its capacity for material discrimination, hyperspectral imaging has been utilized for applications ranging from mining to agriculture to planetary exploration. One of the most common methods of exploiting hyperspectral images is spectral unmixing, which is used to discriminate and locate the various types of materials that are present in the scene. When this processing is done without the aid of a reference library of material spectra, the problem is called blind or unsupervised spectral unmixing. Independent component analysis (ICA) is a blind source separation approach that operates by finding outputs, called independent components, that are statistically independent. ICA has been applied to the unsupervised spectral unmixing problem, producing intriguing, if somewhat unsatisfying results. This dissatisfaction stems from the fact that independent components are subject to a scale ambiguity which must be resolved before they can be used effectively in the context of the spectral unmixing problem. In this dissertation, ICA is explored as a spectral unmixing approach. Various processing steps that are common in many ICA algorithms are examined to assess their impact on spectral unmixing results. Synthetically-generated but physically-realistic data are used to allow the assessment to be quantitative rather than qualitative only. Additionally, two algorithms, class-based abundance rescaling (CBAR) and extended class-based abundance rescaling (CBAR-X), are introduced to enable accurate rescaling of independent components. Experimental results demonstrate the improved rescaling accuracy provided by the CBAR and CBAR-X algorithms, as well as the general viability of ICA as a spectral unmixing approach.

Download or read book Latent Variable Analysis and Signal Separation written by Vincent Vigneron and published by Springer Science & Business Media. This book was released on 2010-09-27 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt: Thisvolumecollectsthepaperspresentedatthe9thInternationalConferenceon Latent Variable Analysis and Signal Separation,LVA/ICA 2010. The conference was organized by INRIA, the French National Institute for Computer Science and Control,and was held in Saint-Malo, France, September 27–30,2010,at the Palais du Grand Large. Tenyearsafterthe?rstworkshoponIndependent Component Analysis(ICA) in Aussois, France, the series of ICA conferences has shown the liveliness of the community of theoreticians and practitioners working in this ?eld. While ICA and blind signal separation have become mainstream topics, new approaches have emerged to solve problems involving signal mixtures or various other types of latent variables: semi-blind models, matrix factorization using sparse com- nent analysis, non-negative matrix factorization, probabilistic latent semantic indexing, tensor decompositions, independent vector analysis, independent s- space analysis, and so on. To re?ect this evolution towards more general latent variable analysis problems in signal processing, the ICA International Steering Committee decided to rename the 9th instance of the conference LVA/ICA. From more than a hundred submitted papers, 25 were accepted as oral p- sentationsand53 asposter presentations. Thecontent ofthis volumefollowsthe conference schedule, resulting in 14 chapters. The papers collected in this v- ume demonstrate that the research activity in the ?eld continues to range from abstract concepts to the most concrete and applicable questions and consid- ations. Speech and audio, as well as biomedical applications, continue to carry the mass of the applications considered.

Download or read book Hybrid Artificial Intelligent Systems written by Emilio S. Corchado Rodriguez and published by Springer Science & Business Media. This book was released on 2012-03-21 with total page 636 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two LNAI volumes 7208 and 7209 constitute the proceedings of the 7th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2012, held in Salamanca, Spain, in March 2012. The 118 papers published in these proceedings were carefully reviewed and selected from 293 submissions. They are organized in topical sessions on agents and multi agents systems, HAIS applications, cluster analysis, data mining and knowledge discovery, evolutionary computation, learning algorithms, systems, man, and cybernetics by HAIS workshop, methods of classifier fusion, HAIS for computer security (HAISFCS), data mining: data preparation and analysis, hybrid artificial intelligence systems in management of production systems, hybrid artificial intelligent systems for ordinal regression, hybrid metaheuristics for combinatorial optimization and modelling complex systems, hybrid computational intelligence and lattice computing for image and signal processing and nonstationary models of pattern recognition and classifier combinations.

Download or read book Hyperspectral Image Analysis written by Saurabh Prasad and published by Springer Nature. This book was released on 2020-04-27 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Hyperspectral Data Processing written by Chein-I Chang and published by John Wiley & Sons. This book was released on 2013-04-08 with total page 1180 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Computer Vision ECCV 2018 Workshops written by Laura Leal-Taixé and published by Springer. This book was released on 2019-01-28 with total page 737 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six-volume set comprising the LNCS volumes 11129-11134 constitutes the refereed proceedings of the workshops that took place in conjunction with the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.43 workshops from 74 workshops proposals were selected for inclusion in the proceedings. The workshop topics present a good orchestration of new trends and traditional issues, built bridges into neighboring fields, and discuss fundamental technologies and novel applications.

Download or read book Comprehensive Chemometrics written by Steven Brown and published by Elsevier. This book was released on 2020-05-26 with total page 2948 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Hyperspectral Imaging written by Chein-I Chang and published by Springer Science & Business Media. This book was released on 2013-12-11 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hyperspectral Imaging: Techniques for Spectral Detection and Classification is an outgrowth of the research conducted over the years in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. It explores applications of statistical signal processing to hyperspectral imaging and further develops non-literal (spectral) techniques for subpixel detection and mixed pixel classification. This text is the first of its kind on the topic and can be considered a recipe book offering various techniques for hyperspectral data exploitation. In particular, some known techniques, such as OSP (Orthogonal Subspace Projection) and CEM (Constrained Energy Minimization) that were previously developed in the RSSIPL, are discussed in great detail. This book is self-contained and can serve as a valuable and useful reference for researchers in academia and practitioners in government and industry.

Download or read book Advances in Independent Component Analysis with Applications to Data Mining written by Ella Bingham and published by . This book was released on 2003 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book OCM 2015 Optical Characterization of Materials conference proceedings written by Beyerer, Juergen and published by KIT Scientific Publishing. This book was released on 2015-03-18 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book An Introduction to Signal Detection and Estimation written by H. Vincent Poor and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 558 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to introduce the reader to the basic theory of signal detection and estimation. It is assumed that the reader has a working knowledge of applied probabil ity and random processes such as that taught in a typical first-semester graduate engineering course on these subjects. This material is covered, for example, in the book by Wong (1983) in this series. More advanced concepts in these areas are introduced where needed, primarily in Chapters VI and VII, where continuous-time problems are treated. This book is adapted from a one-semester, second-tier graduate course taught at the University of Illinois. However, this material can also be used for a shorter or first-tier course by restricting coverage to Chapters I through V, which for the most part can be read with a background of only the basics of applied probability, including random vectors and conditional expectations. Sufficient background for the latter option is given for exam pIe in the book by Thomas (1986), also in this series.

Download or read book Super Resolution Imaging written by Peyman Milanfar and published by CRC Press. This book was released on 2017-12-19 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the exponential increase in computing power and broad proliferation of digital cameras, super-resolution imaging is poised to become the next "killer app." The growing interest in this technology has manifested itself in an explosion of literature on the subject. Super-Resolution Imaging consolidates key recent research contributions from eminent scholars and practitioners in this area and serves as a starting point for exploration into the state of the art in the field. It describes the latest in both theoretical and practical aspects of direct relevance to academia and industry, providing a base of understanding for future progress. Features downloadable tools to supplement material found in the book Recent advances in camera sensor technology have led to an increasingly larger number of pixels being crammed into ever-smaller spaces. This has resulted in an overall decline in the visual quality of recorded content, necessitating improvement of images through the use of post-processing. Providing a snapshot of the cutting edge in super-resolution imaging, this book focuses on methods and techniques to improve images and video beyond the capabilities of the sensors that acquired them. It covers: History and future directions of super-resolution imaging Locally adaptive processing methods versus globally optimal methods Modern techniques for motion estimation How to integrate robustness Bayesian statistical approaches Learning-based methods Applications in remote sensing and medicine Practical implementations and commercial products based on super-resolution The book concludes by concentrating on multidisciplinary applications of super-resolution for a variety of fields. It covers a wide range of super-resolution imaging implementation techniques, including variational, feature-based, multi-channel, learning-based, locally adaptive, and nonparametric methods. This versatile book can be used as the basis for short courses for engineers and scientists, or as part of graduate-level courses in image processing.

Download or read book Comprehensive Remote Sensing written by Shunlin Liang and published by Elsevier. This book was released on 2017-11-08 with total page 3183 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Algorithms and Technologies for Multispectral Hyperspectral and Ultraspectral Imagery written by and published by . This book was released on 2007 with total page 804 pages. Available in PDF, EPUB and Kindle. Book excerpt: