Remote Sensing Image Fusion

Author: Christine Pohl
Editor: CRC Press
ISBN: 1315353067
Size: 13,66 MB
Format: PDF, Mobi
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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.

Kernel Methods For Remote Sensing Data Analysis

Author: Gustau Camp-Valls
Editor: John Wiley & Sons
ISBN: 0470749008
Size: 12,74 MB
Format: PDF, ePub, Docs
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Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have now become the standard approach to many remote sensing applications. With algorithms that combine statistics and geometry, kernel methods have proven successful across many different domains related to the analysis of images of the Earth acquired from airborne and satellite sensors, including natural resource control, detection and monitoring of anthropic infrastructures (e.g. urban areas), agriculture inventorying, disaster prevention and damage assessment, and anomaly and target detection. Presenting the theoretical foundations of kernel methods (KMs) relevant to the remote sensing domain, this book serves as a practical guide to the design and implementation of these methods. Five distinct parts present state-of-the-art research related to remote sensing based on the recent advances in kernel methods, analysing the related methodological and practical challenges: Part I introduces the key concepts of machine learning for remote sensing, and the theoretical and practical foundations of kernel methods. Part II explores supervised image classification including Super Vector Machines (SVMs), kernel discriminant analysis, multi-temporal image classification, target detection with kernels, and Support Vector Data Description (SVDD) algorithms for anomaly detection. Part III looks at semi-supervised classification with transductive SVM approaches for hyperspectral image classification and kernel mean data classification. Part IV examines regression and model inversion, including the concept of a kernel unmixing algorithm for hyperspectral imagery, the theory and methods for quantitative remote sensing inverse problems with kernel-based equations, kernel-based BRDF (Bidirectional Reflectance Distribution Function), and temperature retrieval KMs. Part V deals with kernel-based feature extraction and provides a review of the principles of several multivariate analysis methods and their kernel extensions. This book is aimed at engineers, scientists and researchers involved in remote sensing data processing, and also those working within machine learning and pattern recognition.

Image Analysis Classification And Change Detection In Remote Sensing

Author: Morton J. Canty
Editor: CRC Press
ISBN: 1466570377
Size: 20,34 MB
Format: PDF, ePub
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Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python, Third Edition introduces techniques used in the processing of remote sensing digital imagery. It emphasizes the development and implementation of statistically motivated, data-driven techniques. The author achieves this by tightly interweaving theory, algorithms, and computer codes. See What’s New in the Third Edition: Inclusion of extensive code in Python, with a cloud computing example New material on synthetic aperture radar (SAR) data analysis New illustrations in all chapters Extended theoretical development The material is self-contained and illustrated with many programming examples in IDL. The illustrations and applications in the text can be plugged in to the ENVI system in a completely transparent fashion and used immediately both for study and for processing of real imagery. The inclusion of Python-coded versions of the main image analysis algorithms discussed make it accessible to students and teachers without expensive ENVI/IDL licenses. Furthermore, Python platforms can take advantage of new cloud services that essentially provide unlimited computational power. The book covers both multispectral and polarimetric radar image analysis techniques in a way that makes both the differences and parallels clear and emphasizes the importance of choosing appropriate statistical methods. Each chapter concludes with exercises, some of which are small programming projects, intended to illustrate or justify the foregoing development, making this self-contained text ideal for self-study or classroom use.

An Introduction To Remote Sensing

Author:
Editor:
ISBN: 9781621580997
Size: 13,69 MB
Format: PDF, Docs
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Fundamentals Of Satellite Remote Sensing

Author: Emilio Chuvieco
Editor: CRC Press
ISBN: 9781498728058
Size: 13,27 MB
Format: PDF, ePub, Mobi
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Fundamentals of Satellite Remote Sensing: An Environmental Approach, Second Editionis a definitive guide to remote sensing systems that focuses on satellite-based remote sensing tools and methods for space-based Earth observation (EO). It presents the advantages of using remote sensing data for studying and monitoring the planet, and emphasizes concepts that make the best use of satellite data. The book begins with an introduction to the basic processes that ensure the acquisition of space-borne imagery and provides an overview of the main satellite observation systems. It then describes visual and digital image analysis, highlights various interpretation techniques, and outlines their applications to science and management. The latter part of the book covers the integration of remote sensing with GIS for environmental analysis. Based on the first English version published in 2010, this latest edition has been written to reflect a global audience, and factors in international debates and legal issues surrounding EO, as well as future developments and trends. New in the Second Edition: Includes additional illustrations now in full color Uses sample images acquired from different ecosystems at different spatial resolutions to illustrate different interpretation techniques Updates information on recent satellite missions (Landsat-8, Sentinel-2, hyperspectral and hyperspatial programs) Covers near-ground missions (including UAV) and ground sensors (spectro-radiometers, cameras, LIDAR, etc.) to support EO analysis Offers analysis of image spatial properties Presents material on visual analysis, time series analysis, and data fusion Provides examples of EO data that cover different environmental problems, with particular relevance to global observation Fundamentals of Satellite Remote Sensing: An Environmental Approach, Second Editiondetails the tools that provide global, recurrent, and comprehensive views of the processes affecting the Earth and is a must-have for researchers, academics, students, and professionals involved in the field of environmental science.

Remote Sensing Image Fusion

Author: Luciano Alparone
Editor: CRC Press
ISBN: 1466587504
Size: 19,54 MB
Format: PDF, ePub
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A synthesis of more than ten years of experience, Remote Sensing Image Fusion covers methods specifically designed for remote sensing imagery. The authors supply a comprehensive classification system and rigorous mathematical description of advanced and state-of-the-art methods for pansharpening of multispectral images, fusion of hyperspectral and panchromatic images, and fusion of data from heterogeneous sensors such as optical and synthetic aperture radar (SAR) images and integration of thermal and visible/near-infrared images. They also explore new trends of signal/image processing, such as compressive sensing and sparse signal representations. The book brings a new perspective to a multidisciplinary research field that is becoming increasingly articulate and comprehensive. It fosters signal/image processing methodologies toward the goal of information extraction, either by humans or by machines, from remotely sensed images. The authors explain how relatively simple processing methods tailored to the specific features of the images may be winning in terms of reliable performance over more complex algorithms based on mathematical theories and models unconstrained from the physical behaviors of the instruments. Ultimately, the book covers the births and developments of three generations of RS image fusion. Established textbooks are mainly concerned with the earliest generation of methods. This book focuses on second generation methods you can use now and new trends that may become third generation methods. Only the lessons learned with second generation methods will be capable of fostering the excellence among the myriad of methods that are proposed almost every day by the scientific literature.

Remote Sensing

Author: Robert A. Schowengerdt
Editor: Elsevier
ISBN: 0080516106
Size: 10,75 MB
Format: PDF
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This book is a completely updated, greatly expanded version of the previously successful volume by the author. The Second Edition includes new results and data, and discusses a unified framework and rationale for designing and evaluating image processing algorithms. Written from the viewpoint that image processing supports remote sensing science, this book describes physical models for remote sensing phenomenology and sensors and how they contribute to models for remote-sensing data. The text then presents image processing techniques and interprets them in terms of these models. Spectral, spatial, and geometric models are used to introduce advanced image processing techniques such as hyperspectral image analysis, fusion of multisensor images, and digital elevationmodel extraction from stereo imagery. The material is suited for graduate level engineering, physical and natural science courses, or practicing remote sensing scientists. Each chapter is enhanced by student exercises designed to stimulate an understanding of the material. Over 300 figuresare produced specifically for this book, and numerous tables provide a rich bibliography of the research literature.

Information Processing For Remote Sensing

Author: Chi-hau Chen
Editor: World Scientific
ISBN: 9789810237370
Size: 11,36 MB
Format: PDF, ePub, Docs
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This book provides the most comprehensive study of information processing techniques and issues in remote sensing. Topics covered include image and signal processing, pattern recognition and feature extraction for remote sensing, neural networks and wavelet transforms in remote sensing, remote sensing of ocean and coastal environment, SAR image filtering and segmentation, knowledge-based systems, software and hardware issues, data compression, change detection, etc. Emphasis is placed on environmental issues of remote sensing.With 58 color illustrations.

Understanding Synthetic Aperture Radar Images

Author: Chris Oliver
Editor: SciTech Publishing
ISBN: 1891121316
Size: 11,98 MB
Format: PDF, Kindle
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This practical reference shows SAR system designers and remote sensing specialists how to produce higher quality SAR images using data-driven algorithms, and apply powerful new techniques to measure and analyze SAR image content.

Urban Remote Sensing

Author: Qihao Weng
Editor: CRC Press
ISBN: 1420008803
Size: 14,32 MB
Format: PDF, ePub, Mobi
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Driven by advances in technology and societal needs, the next frontier in remote sensing is urban areas. With the advent of high-resolution imagery and more capable techniques, the question has become "Now that we have the technology, how do we use it?" The need for a definitive resource that explores the technology of remote sensing and the issues it can resolve in an urban setting has never been more acute. Containing contributions from world renowned experts, Urban Remote Sensing provides a review of basic concepts, methodologies, and case studies. Each chapter demonstrates how to apply up-to-date techniques to the problems identified and how to analyze research results. Organized into five sections, this book: Focuses on data, sensors, and systems considerations as well as algorithms for urban feature extraction Analyzes urban landscapes in terms of composition and structure, especially using sub-pixel analysis techniques Presents methods for monitoring, analyzing, and modeling urban growth Illustrates various approaches to urban planning and socio-economic applications of urban remote sensing Assesses the progress made to date, identifies the existing problems and challenges, and demonstrates new developments and trends in urban remote sensing This book is ideal for upper division undergraduate and graduate students, however it can also serve as a reference for researchers or those individuals interested in the remote sensing of cities in academia, and governmental and commercial sectors. Urban Remote Sensing examines how to apply remote sensing technology to urban and suburban areas.