Download or read book High performance Dimension Reduction of Hyperspectral Data written by Sinthop Kaewpijit and published by . This book was released on 2002 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dimensionality Reduction of Hyperspectral Imagery written by Arati Paul and published by Springer Nature. This book was released on 2023-10-04 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides information about different types of dimensionality reduction (DR) methods and their effectiveness in hyperspectral data processing. The authors first explain how hyperspectral imagery (HSI) plays an important role in remote sensing due to its high spectral resolution that enables better identification of different materials on the earth’s surface. The authors go on to describe potential challenges due to HSI being acquired in hundreds of narrow and contiguous bands, represented as a 3-dimensional image cube, often causing the bands to contain information redundancy. They then show how processing a large number of bands adds challenges in terms of computation complexity that reduces efficiency. The authors then present how DR is an essential step in hyperspectral data analysis to solve these issues. Overall, the book helps readers understand the DR processes and its impact in effective HSI analysis.

Download or read book Dimension Reduction for Hyperspectral Imagery written by Nam Hoai Ly and published by . This book was released on 2013 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, the general problem of the dimensionality reduction of hyperspectral imagery is considered. Data dimension can be reduced through compression, in which an original image is encoded into bitstream of greatly reduced size; through application of a transformation, in which a high-dimensional space is mapped into a low-dimensional space; and through a simple process of subsampling, wherein the number of pixels is reduced spatially during image acquisition. All three techniques are investigated in the course of the dissertation. For data compression, an approach to calculate an operational bitrate for JPEG2000 in conjunction with principal component analysis is proposed. It is shown that an optimal bitrate for such a lossy compression method can be estimated while maintaining both class separability as well as anomalous pixels in the original data. On the other hand, the transformation paradigm is studied for spectral dimensionality reduction; specifically, data-independent random spectral projections are considered, while the compressive projection principal component analysis algorithm is adopted for data reconstruction. It is shown that, by incorporating both spectral and spatial partitioning of the original data, reconstruction accuracy can be improved. Additionally, a new supervised spectral dimensionality reduction approach using a sparsity-preserving graph is developed. The resulting sparse graph-based discriminant analysis is seen to yield superior classification performance at low dimensionality. Finally, for spatial dimensionality reduction, a simple spatial subsampling scheme is considered for a multitemporal hyperspectral image sequence, such that the original image is reconstructed using a sparse dictionary learned from a prior image in the sequence.

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 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 Remote Sensing of Vegetation written by Prasad S. Thenkabail and published by CRC Press. This book was released on 2016-04-19 with total page 766 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hyperspectral narrow-band (or imaging spectroscopy) spectral data are fast emerging as practical solutions in modeling and mapping vegetation. Recent research has demonstrated the advances in and merit of hyperspectral data in a range of applications including quantifying agricultural crops, modeling forest canopy biochemical properties, detecting crop stress and disease, mapping leaf chlorophyll content as it influences crop production, identifying plants affected by contaminants such as arsenic, demonstrating sensitivity to plant nitrogen content, classifying vegetation species and type, characterizing wetlands, and mapping invasive species. The need for significant improvements in quantifying, modeling, and mapping plant chemical, physical, and water properties is more critical than ever before to reduce uncertainties in our understanding of the Earth and to better sustain it. There is also a need for a synthesis of the vast knowledge spread throughout the literature from more than 40 years of research. Hyperspectral Remote Sensing of Vegetation integrates this knowledge, guiding readers to harness the capabilities of the most recent advances in applying hyperspectral remote sensing technology to the study of terrestrial vegetation. Taking a practical approach to a complex subject, the book demonstrates the experience, utility, methods and models used in studying vegetation using hyperspectral data. Written by leading experts, including pioneers in the field, each chapter presents specific applications, reviews existing state-of-the-art knowledge, highlights the advances made, and provides guidance for the appropriate use of hyperspectral data in the study of vegetation as well as its numerous applications, such as crop yield modeling, crop and vegetation biophysical and biochemical property characterization, and crop moisture assessment. This comprehensive book brings together the best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, vegetation classification, biophysical and biochemical modeling, crop productivity and water productivity mapping, and modeling. It provides the pertinent facts, synthesizing findings so that readers can get the correct picture on issues such as the best wavebands for their practical applications, methods of analysis using whole spectra, hyperspectral vegetation indices targeted to study specific biophysical and biochemical quantities, and methods for detecting parameters such as crop moisture variability, chlorophyll content, and stress levels. A collective "knowledge bank," it guides professionals to adopt the best practices for their own work.

Download or read book Knowledge Based Intelligent Information and Engineering Systems written by Ignac Lovrek and published by Springer Science & Business Media. This book was released on 2008-08-18 with total page 1079 pages. Available in PDF, EPUB and Kindle. Book excerpt: Annotation The three volume set LNAI 5177, LNAI 5178, and LNAI 5179, constitutes the refereed proceedings of the 12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008, held in Zagreb, Croatia, in September 2008. The 316 revised papers presented were carefully reviewed and selected. The papers present a wealth of original research results from the field of intelligent information processing in the broadest sense; topics covered in the first volume are artificial neural networks and connectionists systems; fuzzy and neuro-fuzzy systems; evolutionary computation; machine learning and classical AI; agent systems; knowledge based and expert systems; intelligent vision and image processing; knowledge management, ontologies, and data mining; Web intelligence, text and multimedia mining and retrieval; and intelligent robotics and control.

Download or read book Software Challenges to Exascale Computing written by Amit Majumdar and published by Springer. This book was released on 2019-04-09 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second Workshop on Software Challenges to Exascale Computing, SCEC 2018, held in Delhi, India, in December 2018. The 10 papers presented in this volume were carefully reviewed and selected from 24 submissions and focus on scientific applications, performance analysis and optimization, science gateways and high-productivity tools and frameworks.

Download or read book Theoretical Computer Science written by Lian Li and published by Springer. This book was released on 2018-09-25 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed proceedings of the National Conference of Theoretical Computer Science, NCTCS 2018, held in Shanghai, China, in October 2018. The 11 full papers presented were carefully reviewed and selected from 31 submissions. They present relevant trends of current research in the area of algorithms and complexity, software theory and method, data science and machine learning theory.

Download or read book Dimensionality Reduction for Hyperspectral Data written by David P. Widemann and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning Approaches for Urban Computing written by Mainak Bandyopadhyay and published by Springer Nature. This book was released on 2021-04-28 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses various machine learning applications and models, developed using heterogeneous data, which helps in a comprehensive prediction, optimization, association analysis, cluster analysis and classification-related applications for various activities in urban area. It details multiple types of data generating from urban activities and suitability of various machine learning algorithms for handling urban data. The book is helpful for researchers, academicians, faculties, scientists and geospatial industry professionals for their research work and sets new ideas in the field of urban computing.

Download or read book Hyperspectral Image Processing written by Liguo Wang and published by Springer. This book was released on 2015-07-15 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on the authors’ research, this book introduces the main processing techniques in hyperspectral imaging. In this context, SVM-based classification, distance comparison-based endmember extraction, SVM-based spectral unmixing, spatial attraction model-based sub-pixel mapping and MAP/POCS-based super-resolution reconstruction are discussed in depth. Readers will gain a comprehensive understanding of these cutting-edge hyperspectral imaging techniques. Researchers and graduate students in fields such as remote sensing, surveying and mapping, geosciences and information systems will benefit from this valuable resource.

Download or read book Advances in Neural Networks ISNN 2007 written by Derong Liu and published by Springer. This book was released on 2007-07-14 with total page 1346 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is part of a three volume set that constitutes the refereed proceedings of the 4th International Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. Coverage includes neural networks for control applications, robotics, data mining and feature extraction, chaos and synchronization, support vector machines, fault diagnosis/detection, image/video processing, and applications of neural networks.

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 Nonlinear Dimensionality Reduction Techniques written by Sylvain Lespinats and published by Springer Nature. This book was released on 2021-12-02 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes tools for analysis of multidimensional and metric data, by establishing a state-of-the-art of the existing solutions and developing new ones. It mainly focuses on visual exploration of these data by a human analyst, relying on a 2D or 3D scatter plot display obtained through Dimensionality Reduction. Performing diagnosis of an energy system requires identifying relations between observed monitoring variables and the associated internal state of the system. Dimensionality reduction, which allows to represent visually a multidimensional dataset, constitutes a promising tool to help domain experts to analyse these relations. This book reviews existing techniques for visual data exploration and dimensionality reduction such as tSNE and Isomap, and proposes new solutions to challenges in that field. In particular, it presents the new unsupervised technique ASKI and the supervised methods ClassNeRV and ClassJSE. Moreover, MING, a new approach for local map quality evaluation is also introduced. These methods are then applied to the representation of expert-designed fault indicators for smart-buildings, I-V curves for photovoltaic systems and acoustic signals for Li-ion batteries.

Download or read book High Performance Computing in Remote Sensing written by Antonio J. Plaza and published by CRC Press. This book was released on 2007-10-18 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book High Performance Heterogeneous Computing written by Jack Dongarra and published by John Wiley & Sons. This book was released on 2009-08-11 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: An analytical overview of the state of the art, open problems, and future trends in heterogeneous parallel and distributed computing This book provides an overview of the ongoing academic research, development, and uses of heterogeneous parallel and distributed computing in the context of scientific computing. Presenting the state of the art in this challenging and rapidly evolving area, the book is organized in five distinct parts: Heterogeneous Platforms: Taxonomy, Typical Uses, and Programming Issues Performance Models of Heterogeneous Platforms and Design of Heterogeneous Algorithms Performance: Implementation and Software Applications Future Tre High Performance Heterogeneous Computing is a valuable¿reference for researchers and practitioners in the area of high performance heterogeneous computing. It also serves as an excellent supplemental text for graduate and postgraduate courses in related areas.