Multi-Spectral Signal and Its Processing
Multi-Spectral Signal and Its Processing

Author: Melinda

Publisher: Syiah Kuala University Press

Published: 2022-05-31

Total Pages: 104

ISBN-13: 6232645707

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An event that rises and falls in the peak value of the amplitude of a certain data as measured through the data acquisition process is known as fluctuation. Fluctuations usually occur because the data obtained during the acquisition process is mixed with noise. Therefore, an analytical approach is needed that can process signal fluctuations to identify the characteristics of a material. This study uses an object made of H2O material used as a measurement platform or footing. The other ingredients are H2O mixed with HCl and H2O mixed with NaOH. The initial processing approach is related to the material identification system using a capacitive sensor based on the Impedance Spectroscopy (SI) method. This study aims to develop a method for processing multi-frequency signal fluctuations resulting from data acquisition of Multi-Spectral Capacitive Sensors (MSCS). An approach to representing the observed fluctuations in data acquisition results is based on the statistical mean and standard deviation of the observed noise spectral in a large number of data sets. The results of signal fluctuations are divided into several types, namely: Mean Fluctuation (MF), High Fluctuation (HF), and High High-Fluctuation (HHF). Several approaches are taken for processing fluctuations, such as the data consistency process to see the stability of the data from the initial processing stage. Next is the stage of grouping data with several new approach methods. Another method that we use is the segmentation method which uses several filters that can divide some signals in the form of fluctuation patterns into several segments. From several approach methods that have been carried out, the results show that some of these methods can identify multi-spectral fluctuation patterns so that it will be easier for the next identification process.

Hyperspectral Data Processing
Hyperspectral Data Processing

Author: Chein-I Chang

Publisher: John Wiley & Sons

Published: 2013-02-01

Total Pages: 1180

ISBN-13: 1118269772

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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 Analysis
Hyperspectral Image Analysis

Author: Saurabh Prasad

Publisher: Springer Nature

Published: 2020-04-27

Total Pages: 464

ISBN-13: 3030386171

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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.

Signal Processing for Neuroscientists
Signal Processing for Neuroscientists

Author: Wim van Drongelen

Publisher: Elsevier

Published: 2006-12-18

Total Pages: 319

ISBN-13: 008046775X

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Signal Processing for Neuroscientists introduces analysis techniques primarily aimed at neuroscientists and biomedical engineering students with a reasonable but modest background in mathematics, physics, and computer programming. The focus of this text is on what can be considered the 'golden trio' in the signal processing field: averaging, Fourier analysis, and filtering. Techniques such as convolution, correlation, coherence, and wavelet analysis are considered in the context of time and frequency domain analysis. The whole spectrum of signal analysis is covered, ranging from data acquisition to data processing; and from the mathematical background of the analysis to the practical application of processing algorithms. Overall, the approach to the mathematics is informal with a focus on basic understanding of the methods and their interrelationships rather than detailed proofs or derivations. One of the principle goals is to provide the reader with the background required to understand the principles of commercially available analyses software, and to allow him/her to construct his/her own analysis tools in an environment such as MATLAB®. - Multiple color illustrations are integrated in the text - Includes an introduction to biomedical signals, noise characteristics, and recording techniques - Basics and background for more advanced topics can be found in extensive notes and appendices - A Companion Website hosts the MATLAB scripts and several data files: http://www.elsevierdirect.com/companion.jsp?ISBN=9780123708670

Multispectral Image Processing And Pattern Recognition
Multispectral Image Processing And Pattern Recognition

Author: Jun Shen

Publisher: World Scientific

Published: 2001-06-04

Total Pages: 135

ISBN-13: 981449125X

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Contents:Introduction (J Shen et al.)3D Articulated Object Understanding, Learning, and Recognition from 2D Images (P S P Wang)On Geometric and Orthogonal Moments (J Shen et al.)Multispectral Image Processing: The Nature Factor (W R Watkins)Detection of Sea Surface Small Targets in Infrared Images Based on Multilevel Filter and Minimum Risk Bayes Test (Y-S Moon et al.)Minimum Description Length Method for Facet Matching (S Maybank & R Fraile)An Integrated Vision System for ALV Navigation (X Ye et al.)Fuzzy Bayesian Networks — A General Formalism for Representation, Inference and Learning with Hybrid Bayesian Networks (H Pan & L Liu)Extraction of Bibliography Information Based on Image of Book Cover (H Yang et al.)Radar Target Recognition Based on Parameterized High Resolution Range Profiles (X Liao & Z Bao) Readership: Computer scientists and electrical engineers. Keywords:

Multimodal Scene Understanding
Multimodal Scene Understanding

Author: Michael Ying Yang

Publisher: Academic Press

Published: 2019-07-16

Total Pages: 424

ISBN-13: 0128173599

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Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. - Contains state-of-the-art developments on multi-modal computing - Shines a focus on algorithms and applications - Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning

Hyperspectral Data Processing
Hyperspectral Data Processing

Author: Chein-I Chang

Publisher: John Wiley & Sons

Published: 2013-04-08

Total Pages: 1180

ISBN-13: 0471690562

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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.

Graph Spectral Image Processing
Graph Spectral Image Processing

Author: Gene Cheung

Publisher: John Wiley & Sons

Published: 2021-08-31

Total Pages: 322

ISBN-13: 1789450284

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Graph spectral image processing is the study of imaging data from a graph frequency perspective. Modern image sensors capture a wide range of visual data including high spatial resolution/high bit-depth 2D images and videos, hyperspectral images, light field images and 3D point clouds. The field of graph signal processing – extending traditional Fourier analysis tools such as transforms and wavelets to handle data on irregular graph kernels – provides new flexible computational tools to analyze and process these varied types of imaging data. Recent methods combine graph signal processing ideas with deep neural network architectures for enhanced performances, with robustness and smaller memory requirements. The book is divided into two parts. The first is centered on the fundamentals of graph signal processing theories, including graph filtering, graph learning and graph neural networks. The second part details several imaging applications using graph signal processing tools, including image and video compression, 3D image compression, image restoration, point cloud processing, image segmentation and image classification, as well as the use of graph neural networks for image processing.

Signal Theory Methods in Multispectral Remote Sensing
Signal Theory Methods in Multispectral Remote Sensing

Author: David A Landgrebe

Publisher: John Wiley & Sons

Published: 2005-02-04

Total Pages: 528

ISBN-13: 0471721255

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An outgrowth of the author's extensive experience teaching senior and graduate level students, this is both a thorough introduction and a solid professional reference. * Material covered has been developed based on a 35-year research program associated with such systems as the Landsat satellite program and later satellite and aircraft programs. * Covers existing aircraft and satellite programs and several future programs *An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.