Kernel Methods For Remote Sensing Data Analysis
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Kernel Methods For Remote Sensing Data Analysis
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Author : Gustau Camps-Valls
language : en
Publisher: John Wiley & Sons
Release Date : 2009-09-03
Kernel Methods For Remote Sensing Data Analysis written by Gustau Camps-Valls and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-09-03 with Technology & Engineering categories.
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.
Remote Sensing Image Processing
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Author : Gustavo Camps-Valls
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Remote Sensing Image Processing written by Gustavo Camps-Valls and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Technology & Engineering categories.
Earth observation is the field of science concerned with the problem of monitoring and modeling the processes on the Earth surface and their interaction with the atmosphere. The Earth is continuously monitored with advanced optical and radar sensors. The images are analyzed and processed to deliver useful products to individual users, agencies and public administrations. To deal with these problems, remote sensing image processing is nowadays a mature research area, and the techniques developed in the field allow many real-life applications with great societal value. For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical and environmental issues. To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics and communications. From a machine learning and signal/image processing point of view, all the applications are tackled under specific formalisms, such as classification and clustering, regression and function approximation, data coding, restoration and enhancement, source unmixing, data fusion or feature selection and extraction. This book covers some of the fields in a comprehensive way. Table of Contents: Remote Sensing from Earth Observation Satellites / The Statistics of Remote Sensing Images / Remote Sensing Feature Selection and Extraction / Classification / Spectral Mixture Analysis / Estimation of Physical Parameters
Optical Remote Sensing
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Author : Saurabh Prasad
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-03-23
Optical Remote Sensing written by Saurabh Prasad and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-23 with Technology & Engineering categories.
Optical remote sensing relies on exploiting multispectral and hyper spectral imagery possessing high spatial and spectral resolutions respectively. These modalities, although useful for most remote sensing tasks, often present challenges that must be addressed for their effective exploitation. This book presents current state-of-the-art algorithms that address the following key challenges encountered in representation and analysis of such optical remotely sensed data. Challenges in pre-processing images, storing and representing high dimensional data, fusing different sensor modalities, pattern classification and target recognition, visualization of high dimensional imagery.
Parallel Programming Models And Applications In Grid And P2p Systems
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Author : F. Xhafa
language : en
Publisher: IOS Press
Release Date : 2009-06-04
Parallel Programming Models And Applications In Grid And P2p Systems written by F. Xhafa and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-04 with Computers categories.
The demand for more computing power has been a constant trend in many fields of science, engineering and business. Now more than ever, the need for more and more processing power is emerging in the resolution of complex problems from life sciences, financial services, drug discovery, weather forecasting, massive data processing for e-science, e-commerce and e-government etc. Grid and P2P paradigms are based on the premise to deliver greater computing power at less cost, thus enabling the solution of such complex problems. Parallel Programming, Models and Applications in Grid and P2P Systems presents recent advances for grid and P2P paradigms, middleware, programming models, communication libraries, as well as their application to the resolution of real-life problems. By approaching grid and P2P paradigms in an integrated and comprehensive way, we believe that this book will serve as a reference for researchers and developers of the grid and P2P computing communities. Important features of the book include an up-to-date survey of grid and P2P programming models, middleware and communication libraries, new approaches for modeling and performance analysis in grid and P2P systems, novel grid and P2P middleware as well as grid and P2P-enabled applications for real-life problems. Academics, scientists, software developers and engineers interested in the grid and P2P paradigms will find the comprehensive coverage of this book useful for their academic, research and development activity.
Hyperspectral Remote Sensing
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Author : Prem Chandra Pandey
language : en
Publisher: Elsevier
Release Date : 2020-08-05
Hyperspectral Remote Sensing written by Prem Chandra Pandey and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-05 with Science categories.
Hyperspectral Remote Sensing: Theory and Applications offers the latest information on the techniques, advances and wide-ranging applications of hyperspectral remote sensing, such as forestry, agriculture, water resources, soil and geology, among others. The book also presents hyperspectral data integration with other sources, such as LiDAR, Multi-spectral data, and other remote sensing techniques. Researchers who use this resource will be able to understand and implement the technology and data in their respective fields. As such, it is a valuable reference for researchers and data analysts in remote sensing and Earth Observation fields and those in ecology, agriculture, hydrology and geology. - Includes the theory of hyperspectral remote sensing, along with techniques and applications across a variety of disciplines - Presents the processing, methods and techniques utilized for hyperspectral remote sensing and in-situ data collection - Provides an overview of the state-of-the-art, including algorithms, techniques and case studies
Geological Disaster Monitoring Based On Sensor Networks
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Author : Tariq S. Durrani
language : en
Publisher: Springer
Release Date : 2018-08-09
Geological Disaster Monitoring Based On Sensor Networks written by Tariq S. Durrani and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-09 with Science categories.
This book presents the outcomes of the workshop sponsored by the National Natural Sciences Foundation of China and the UK Newton Fund, British Council Researcher Links. The Workshop was held in Harbin, China, from 14 to 17 July 2017, and brought together some thirty young (postdoctoral) researchers from China and the UK specializing in geosciences, sensor signal networks and their applications to natural disaster recovery. The Workshop presentations covered the state of the art in the area of disaster recovery and blended wireless sensor systems that act as early warning systems to mitigate the consequences of disasters and function as post-disaster recovery vehicles. This book promotes knowledge transfer and helps readers explore and identify research opportunities by highlighting research outcomes in the internationally relevant area of disaster recovery and mitigation.
Fuzzy Machine Learning Algorithms For Remote Sensing Image Classification
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Author : Anil Kumar
language : en
Publisher: CRC Press
Release Date : 2020-07-19
Fuzzy Machine Learning Algorithms For Remote Sensing Image Classification written by Anil Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-19 with Computers categories.
This book covers the state-of-art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance, forest fire mapping, stubble burning, post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well, this book addresses other related algorithms based on distance, kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels, non-linearity and noisy pixels. Further, this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate, postgraduate, research scholars and working professionals of different branches such as Geoinformation sciences, Geography, Electrical, Electronics and Computer Sciences etc., working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields, for example, in medical imaging. Overall, this book aims to: exclusive focus on using large range of fuzzy classification algorithms for remote sensing images; discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images; describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms; explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool, Entropy, Correlation Coefficient, Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and; combines explanation of the algorithms with case studies and practical applications.
Hyperspectral Imaging
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Author :
language : en
Publisher: Elsevier
Release Date : 2019-09-29
Hyperspectral Imaging written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-29 with Science categories.
Hyperspectral Imaging, Volume 32, presents a comprehensive exploration of the different analytical methodologies applied on hyperspectral imaging and a state-of-the-art analysis of applications in different scientific and industrial areas. This book presents, for the first time, a comprehensive collection of the main multivariate algorithms used for hyperspectral image analysis in different fields of application. The benefits, drawbacks and suitability of each are fully discussed, along with examples of their application. Users will find state-of-the art information on the machinery for hyperspectral image acquisition, along with a critical assessment of the usage of hyperspectral imaging in diverse scientific fields. - Provides a comprehensive roadmap of hyperspectral image analysis, with benefits and considerations for each method discussed - Covers state-of-the-art applications in different scientific fields - Discusses the implementation of hyperspectral devices in different environments
Comprehensive Remote Sensing
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Author : Shunlin Liang
language : en
Publisher: Elsevier
Release Date : 2017-11-08
Comprehensive Remote Sensing written by Shunlin Liang and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-08 with Science categories.
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
Learning To Understand Remote Sensing Images
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Author : Qi Wang
language : en
Publisher: MDPI
Release Date : 2019-09-30
Learning To Understand Remote Sensing Images written by Qi Wang and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-30 with Computers categories.
With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.