In today's world, deep learning source codes and a plethora of open access geospatial images are readily available and easily accessible. However, most people are missing the educational tools to make use of this resource. Deep Learning for Remote Sensing Images with Open Source Software is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches detailed in this book are generic and can be adapted to suit many different applications for remote sensing image processing, including landcover mapping, forestry, urban studies, disaster mapping, image restoration, etc. Written with practitioners and students in mind, this book helps link together the theory and practical use of existing tools and data to apply deep learning techniques on remote sensing images and data. Specific Features of this Book: The first book that explains how to apply deep learning techniques to public, free available data (Spot-7 and Sentinel-2 images, OpenStreetMap vector data), using open source software (QGIS, Orfeo ToolBox, TensorFlow) Presents approaches suited for real world images and data targeting large scale processing and GIS applications Introduces state of the art deep learning architecture families that can be applied to remote sensing world, mainly for landcover mapping, but also for generic approaches (e.g. image restoration) Suited for deep learning beginners and readers with some GIS knowledge. No coding knowledge is required to learn practical skills. Includes deep learning techniques through many step by step remote sensing data processing exercises.

The use of synthetic aperture radar (SAR) represents a new era in remote sensing technology. A complete handbook for anyone who must design an SAR system capable of reliably producing high quality image data products, free from image artifacts and calibrated in terms of the target backscatter coefficient. Combines fundamentals underlying the SAR imaging process and the practical system engineering required to produce quality images from a real SAR system. Beginning with a broad overview of SAR technology, it goes on to examine SAR system capabilities and components and detail the techniques required for design and development of the SAR ground data system with emphasis on the correlation processing. Intended for SAR system engineers and researchers, it is generously illustrated for maximum clarity.

Remote sensing of impervious surfaces has matured using advances in geospatial technology so recent that its applications have received only sporadic coverage in remote sensing literature. Remote Sensing of Impervious Surfaces is the first to focus entirely on this developing field. It provides detailed coverage of mapping, data extraction, and modeling techniques specific to analyzing impervious surfaces, such as roads and buildings. Written by renowned experts in the field, this book reviews the major approaches that apply to this emerging field as well as current challenges, developments, and trends. The authors introduce remote sensing digital image processing techniques for estimating and mapping impervious surfaces in urban and rural areas. Presenting the latest modeling tools and algorithms for data extraction and analysis, the book explains how to differentiate roads, roofs, and other manmade structures from remotely sensed images for individual analysis. The final chapters examine how to use impervious surface data for predicting the flow of storm- or floodwater and studying trends in population, land use, resource distribution, and other real-world applications in environmental, urban, and regional planning. Each chapter offers a consistent format including a concise review of basic concepts and methodologies, timely case studies, and guidance for solving problems and analyzing data using the techniques presented.

Over the past decade, the field of image processing has made tremendous advances. One type of image processing that is currently of particular interest is "tomographic imaging," a technique for computing the density function of a body, or discontinuity surfaces of this function. Today, tomography is widely used, and has applications in such fields as medicine, engineering, physics, geophysics, and security. The Radon Transform and Local Tomography clearly explains the theoretical, computational, and practical aspects of applied tomography. It includes sufficient background information to make it essentially self-contained for most readers.

Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications. Incorporating both existing and new ideas, the book conveniently gives you one-stop access to a number of different decompositions, algorithms, implementations, and benchmarking techniques. Divided into five parts, the book begins with an overall introduction to robust principal component analysis (PCA) via decomposition into low-rank and sparse matrices. The second part addresses robust matrix factorization/completion problems while the third part focuses on robust online subspace estimation, learning, and tracking. Covering applications in image and video processing, the fourth part discusses image analysis, image denoising, motion saliency detection, video coding, key frame extraction, and hyperspectral video processing. The final part presents resources and applications in background/foreground separation for video surveillance. With contributions from leading teams around the world, this handbook provides a complete overview of the concepts, theories, algorithms, and applications related to robust low-rank and sparse matrix decompositions. It is designed for researchers, developers, and graduate students in computer vision, image and video processing, real-time architecture, machine learning, and data mining.

This book provides insights into the benefits of using remote sensing data from a geoscientist's perspective, by integrating the data with the understanding of Earth's surface and subsurface. In 3 sections, the book takes a detailed look at what data explorationists use when they explore for hydrocarbon resources, assess different terrain types for planning and hazards and extract present-day geologic analogs for subsurface geologic settings. The book presents the usage of remote sensing data in exploration in a structured way by detecting individual geologic features as building blocks for complex geologic systems. This concept enables readers to build their own workflows for the assessment of complex geologic systems using various combinations of remote sensing data. Section 1 introduces readers to the foundations of remote sensing for exploration, covers various methods of image processing and studies different digital elevation and bathymetry models. Section 2 presents the concept of geomorphology as a means to integrate surface and subsurface data. Different aspects of rendering in 2D and 3D are explained and used for the interpretation and extraction of geologic features that are used in exploration. Section 3 addresses remote sensing for hydrocarbon exploration in detail, from geophysical data acquisition to development and infrastructure planning. The organization of this chapter follows an exploration workflow from regional to local modeling studying basin and petroleum system modeling as well as logistics planning of seismic surveys and near-surface modeling. Aspects of field development and infrastructure planning comprise multi-temporal and dynamic modeling. The section closes with a structured approach to extracting geologic analogs from interpreted remote sensing data. The book will be of interest to professionals and students working in exploration for hydrocarbons and water resources, as well as geoscientists and engineers using remote sensing for infrastructure planning, hazard assessment and dynamic environmental studies.

Image Processing and Acquisition using Python provides readers with a sound foundation in both image acquisition and image processing-one of the first books to integrate these topics together. By improving readers' knowledge of image acquisition techniques and corresponding image processing, the book will help them perform experiments more effectively and cost efficiently as well as analyze and measure more accurately. Long recognized as one of the easiest languages for non-programmers to learn, Python is used in a variety of practical examples. A refresher for more experienced readers, the first part of the book presents an introduction to Python, Python modules, reading and writing images using Python, and an introduction to images. The second part discusses the basics of image processing, including pre/post processing using filters, segmentation, morphological operations, and measurements. The second part describes image acquisition using various modalities, such as x-ray, CT, MRI, light microscopy, and electron microscopy. These modalities encompass most of the common image acquisition methods currently used by researchers in academia and industry. Features Covers both the physical methods of obtaining images and the analytical processing methods required to understand the science behind the images. Contains many examples, detailed derivations, and working Python examples of the techniques. Offers practical tips on image acquisition and processing. Includes numerous exercises to test the reader's skills in Python programming and image processing, with solutions to selected problems, example programs, and images available on the book's web page. New to this edition Machine learning has become an indispensable part of image processing and computer vision, so in this new edition two new chapters are included: one on neural networks and the other on convolutional neural networks. A new chapter on affine transform and many new algorithms. Updated Python code aligned to the latest version of modules.

Medical image analysis using advanced fuzzy set theoretic techniques is an exciting and dynamic branch of image processing. Since the introduction of fuzzy set theory, there has been an explosion of interest in advanced fuzzy set theories-such as intuitionistic fuzzy and Type II fuzzy set-that represent uncertainty in a better way. Medical Image Processing: Advanced Fuzzy Set Theoretic Techniques deals with the application of intuitionistic fuzzy and Type II fuzzy set theories for medical image analysis. Designed for graduate and doctorate students, this higher-level text: Provides a brief introduction to advanced fuzzy set theory, fuzzy/intuitionistic fuzzy aggregation operators, and distance/similarity measures Covers medical image enhancement using advanced fuzzy sets, including MATLAB (R)-based examples to increase contrast of the images Describes intuitionistic fuzzy and Type II fuzzy thresholding techniques that separate different regions/leukocyte types/abnormal lesions Demonstrates the clustering of unwanted lesions/regions even in the presence of noise by applying intuitionistic fuzzy clustering Highlights the edges of poorly illuminated images and uses intuitionistic fuzzy edge detection to find the edges of different regions Defines fuzzy mathematical morphology and explores its application using the Lukasiewicz operator, t-norms, and t-conorms Medical Image Processing: Advanced Fuzzy Set Theoretic Techniques is useful not only for students, but also for teachers, engineers, scientists, and those interested in the field of medical image analysis. A basic knowledge of fuzzy set is required, along with a solid understanding of mathematics and image processing.

The main subject of the monograph is the fractional calculus in the discrete version. The volume is divided into three main parts. Part one contains a theoretical introduction to the classical and fractional-order discrete calculus where the fundamental role is played by the backward difference and sum. In the second part, selected applications of the discrete fractional calculus in the discrete system control theory are presented. In the discrete system identification, analysis and synthesis, one can consider integer or fractional models based on the fractional-order difference equations. The third part of the book is devoted to digital image processing.

Das Fachbuch liefert erstmals einen umfassenden Uberblick uber Konzeption, Produktion und Anwendung von 360 -Welten und erkundet damit die Potenziale eines neuen Marktes. Experten vermitteln den aktuellen Stand zu Themen wie den neurobiologischen Grundlagen, Story-Telling, Drehbuch, Visual Effects oder Projektionstechnologie. Daruber hinaus diskutieren sie Marketing- und Vertriebsfragen und geben einen Ausblick auf den Einsatz von Fullspace-Projektionen in Bildung, Entertainment und Medizin. Mit Projektskizzen als Beispiele fur Praxisanwendungen.

This is the third volume in a trilogy on modern Signal Processing. The three books provide a concise exposition of signal processing topics, and a guide to support individual practical exploration based on MATLAB programs. This book includes MATLAB codes to illustrate each of the main steps of the theory, offering a self-contained guide suitable for independent study. The code is embedded in the text, helping readers to put into practice the ideas and methods discussed. The book primarily focuses on filter banks, wavelets, and images. While the Fourier transform is adequate for periodic signals, wavelets are more suitable for other cases, such as short-duration signals: bursts, spikes, tweets, lung sounds, etc. Both Fourier and wavelet transforms decompose signals into components. Further, both are also invertible, so the original signals can be recovered from their components. Compressed sensing has emerged as a promising idea. One of the intended applications is networked devices or sensors, which are now becoming a reality; accordingly, this topic is also addressed. A selection of experiments that demonstrate image denoising applications are also included. In the interest of reader-friendliness, the longer programs have been grouped in an appendix; further, a second appendix on optimization has been added to supplement the content of the last chapter.

Remote Sensing Image Fusion: A Practical Guide gives an introduction to remote sensing image fusion providing an overview on the sensors and applications. It describes data selection, application requirements and the choice of a suitable image fusion technique. It comprises a diverse selection of successful image fusion cases that are relevant to other users and other areas of interest around the world. The book helps newcomers to obtain a quick start into the practical value and benefits of multi-sensor image fusion. Experts will find this book useful to obtain an overview on the state of the art and understand current constraints that need to be solved in future research efforts. For industry professionals the book can be a great introduction and basis to understand multisensor remote sensing image exploitation and the development of commercialized image fusion software from a practical perspective. The book concludes with a chapter on current trends and future developments in remote sensing image fusion. Along with the book, RSIF website provides additional up-to-date information in the field.

Sir William Rowan Hamilton was a genius, and will be remembered for his significant contributions to physics and mathematics. The Hamiltonian, which is used in quantum physics to describe the total energy of a system, would have been a major achievement for anyone, but Hamilton also invented quaternions, which paved the way for modern vector analysis.

Quaternions are one of the most documented inventions in the history of mathematics, and this book is about their invention, and how they are used to rotate vectors about an arbitrary axis. Apart from introducing the reader to the features of quaternions and their associated algebra, the book provides valuable historical facts that bring the subject alive.

Quaternions for Computer Graphics introduces the reader to quaternion algebra by describing concepts of sets, groups, fields and rings. It also includes chapters on imaginary quantities, complex numbers and the complex plane, which are essential to understanding quaternions. The book contains many illustrations and worked examples, which make it essential reading for students, academics, researchers and professional practitioners.

Taking a novel, more appealing approach than current texts, An Integrated Introduction to Computer Graphics and Geometric Modeling focuses on graphics, modeling, and mathematical methods, including ray tracing, polygon shading, radiosity, fractals, freeform curves and surfaces, vector methods, and transformation techniques. The author begins with fractals, rather than the typical line-drawing algorithms found in many standard texts. He also brings the turtle back from obscurity to introduce several major concepts in computer graphics. Supplying the mathematical foundations, the book covers linear algebra topics, such as vector geometry and algebra, affine and projective spaces, affine maps, projective transformations, matrices, and quaternions. The main graphics areas explored include reflection and refraction, recursive ray tracing, radiosity, illumination models, polygon shading, and hidden surface procedures. The book also discusses geometric modeling, including planes, polygons, spheres, quadrics, algebraic and parametric curves and surfaces, constructive solid geometry, boundary files, octrees, interpolation, approximation, Bezier and B-spline methods, fractal algorithms, and subdivision techniques. Making the material accessible and relevant for years to come, the text avoids descriptions of current graphics hardware and special programming languages. Instead, it presents graphics algorithms based on well-established physical models of light and cogent mathematical methods.

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.

The book familiarizes readers with fundamental concepts and issues related to computer vision and major approaches that address them. The focus of the book is on image acquisition and image formation models, radiometric models of image formation, image formation in the camera, image processing concepts, concept of feature extraction and feature selection for pattern classification/recognition, and advanced concepts like object classification, object tracking, image-based rendering, and image registration. Intended to be a companion to a typical teaching course on computer vision, the book takes a problem-solving approach.

This book proceedings collects a number of papers presented at the International Conference on Sensing and Imaging, which was held at Guangxi University of Science and Technology from October 15-18, 2018. Sensing and imaging is an interdisciplinary field covering a variety of sciences and techniques such as optics, electricity, magnetism, heat, sound, and computing technologies. The field has diverse applications of interest such as image processing techniques.The results in the book bridge the gap between theory and applications, translating techniques into better products. The text will appeal to students, professionals and researchers alike.

This book provides a unique, in-depth discussion of multiview learning, one of the fastest developing branches in machine learning. Multiview Learning has been proved to have good theoretical underpinnings and great practical success. This book describes the models and algorithms of multiview learning in real data analysis. Incorporating multiple views to improve the generalization performance, multiview learning is also known as data fusion or data integration from multiple feature sets. This self-contained book is applicable for multi-modal learning research, and requires minimal prior knowledge of the basic concepts in the field. It is also a valuable reference resource for researchers working in the field of machine learning and also those in various application domains.

Goals of the Book Overthelast thirty yearsthere has been arevolutionindiagnostic radiology as a result oftheemergenceofcomputerized tomography (CT), which is the process of obtaining the density distribution within the human body from multiple x-ray projections. Since an enormous variety of possible density values may occur in the body, a large number of projections are necessary to ensure the accurate reconstruction oftheir distribution. There are other situations in which we desire to reconstruct an object from its projections, but in which we know that the object to be recon structed has only a small number of possible values. For example, a large fraction of objects scanned in industrial CT (for the purpose of nonde structive testing or reverse engineering) are made of a single material and so the ideal reconstruction should contain only two values: zero for air and the value associated with the material composing the object. Similar as sumptions may even be made for some specific medical applications; for example, in angiography ofthe heart chambers the value is either zero (in dicating the absence of dye) or the value associated with the dye in the chamber. Another example arises in the electron microscopy of biological macromolecules, where we may assume that the object to be reconstructed is composed of ice, protein, and RNA. One can also apply electron mi croscopy to determine the presenceor absence ofatoms in crystallinestruc tures, which is again a two-valued situation."

This book constitutes selected, revised and extended papers from the 13th International Conference on Computer Supported Education, CSEDU 2021, held as a virtual event in April 2021. The 27 revised full papers were carefully reviewed and selected from 143 submissions. They were organized in topical sections as follows: artificial intelligence in education; information technologies supporting learning; learning/teaching methodologies and assessment; social context and learning environments; ubiquitous learning; current topics.

Marking a distinct departure from the perspectives of frame theory and discrete transforms, this book provides a comprehensive mathematical and algorithmic introduction to wavelet theory. As such, it can be used as either a textbook or reference guide. As a textbook for graduate mathematics students and beginning researchers, it offers detailed information on the basic theory of framelets and wavelets, complemented by self-contained elementary proofs, illustrative examples/figures, and supplementary exercises. Further, as an advanced reference guide for experienced researchers and practitioners in mathematics, physics, and engineering, the book addresses in detail a wide range of basic and advanced topics (such as multiwavelets/multiframelets in Sobolev spaces and directional framelets) in wavelet theory, together with systematic mathematical analysis, concrete algorithms, and recent developments in and applications of framelets and wavelets. Lastly, the book can also be used to teach on or study selected special topics in approximation theory, Fourier analysis, applied harmonic analysis, functional analysis, and wavelet-based signal/image processing.

This is the first volume in a trilogy on modern Signal Processing. The three books provide a concise exposition of signal processing topics, and a guide to support individual practical exploration based on MATLAB programs. This book includes MATLAB codes to illustrate each of the main steps of the theory, offering a self-contained guide suitable for independent study. The code is embedded in the text, helping readers to put into practice the ideas and methods discussed. The book is divided into three parts, the first of which introduces readers to periodic and non-periodic signals. The second part is devoted to filtering, which is an important and commonly used application. The third part addresses more advanced topics, including the analysis of real-world non-stationary signals and data, e.g. structural fatigue, earthquakes, electro-encephalograms, birdsong, etc. The book's last chapter focuses on modulation, an example of the intentional use of non-stationary signals.

The Book presents an overview of newly developed watermarking techniques in various independent and hybrid domains Covers the basics of digital watermarking, its types, domain in which it is implemented and the application of machine learning algorithms onto digital watermarking Reviews hardware implementation of watermarking Discusses optimization problems and solutions in watermarking with a special focus on bio-inspired algorithms Includes a case study along with its MATLAB code and simulation results

This book gathers papers presented at the Workshop on Computational Diffusion MRI, CDMRI 2020, held under the auspices of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), which took place virtually on October 8th, 2020, having originally been planned to take place in Lima, Peru.This book presents the latest developments in the highly active and rapidly growing field of diffusion MRI. While offering new perspectives on the most recent research challenges in the field, the selected articles also provide a valuable starting point for anyone interested in learning computational techniques for diffusion MRI. The book includes rigorous mathematical derivations, a large number of rich, full-colour visualizations, and clinically relevant results. As such, it is of interest to researchers and practitioners in the fields of computer science, MRI physics, and applied mathematics. The reader will find numerous contributions covering a broad range of topics, from the mathematical foundations of the diffusion process and signal generation to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as diffusion-relaxometry and frontline applications in research and clinical practice.

Edited by a renowned international expert in the field, Nuclear Medicine Physics offers an up-to-date, state-of-the-art account of the physics behind the theoretical foundation and applications of nuclear medicine. It covers important physical aspects of the methods and instruments involved in modern nuclear medicine, along with related biological topics. The book first discusses the physics of and machines for producing radioisotopes suitable for use in conventional nuclear medicine and PET. After focusing on positron physics and the applications of positrons in medicine and biology, it describes the use of radiopharmaceuticals in molecular imaging, clinical, and research studies. The text then covers modern radiation detectors and measuring methods, including those used in nuclear imaging, as well as numerous imaging methodologies and models, such as two- and three-dimensional image reconstruction algorithms, data processing sequences, new nuclear oncology techniques, and physiological models of the central nervous system. It also introduces biological systems theory, nuclear medicine methods as systems theory procedures, and aspects of kinetic modeling. The final chapter explores dosimetry and the biological effects of ionizing radiation. With many new developments occurring in nuclear medicine, it is important to understand how advanced approaches are being used in emerging applications. Offering invaluable insight into this growth, Nuclear Medicine Physics provides in-depth descriptions of new radiolabeled biological drugs, new cell labeling techniques, new technical concepts in radiation detection, improvements in instrumentation, and much more.