This authoritative reference offers you a comprehensive understanding of the underpinnings and practical applications of artificial neural networks and their use in the retrieval of geophysical parameters. You find expert guidance on the development and evaluation of neural network algorithms that process data from a new generation of hyperspectral sensors. The book provides clear explanations of the mathematical and physical foundations of remote sensing systems, including radiative transfer and propagation theory, sensor technologies, and inversion and estimation approaches. You discover how to use neural networks to approximate remote sensing inverse functions with emphasis on model selection, preprocessing, initialization, training, and performance evaluation.

This book brings together a representative set of Earth System Science (ESS) applications of the neural network (NN) technique. It examines a progression of atmospheric and oceanic problems, which, from the mathematical point of view, can be formulated as complex, multidimensional, and nonlinear mappings. It is shown that these problems can be solved utilizing a particular type of NN – the multilayer perceptron (MLP). This type of NN applications covers the majority of NN applications developed in ESSs such as meteorology, oceanography, atmospheric and oceanic satellite remote sensing, numerical weather prediction, and climate studies. The major properties of the mappings and MLP NNs are formulated and discussed. Also, the book presents basic background for each introduced application and provides an extensive set of references. “This is an excellent book to learn how to apply artificial neural network methods to earth system sciences. The author, Dr. Vladimir Krasnopolsky, is a universally recognized master in this field. With his vast knowledge and experience, he carefully guides the reader through a broad variety of problems found in the earth system sciences where neural network methods can be applied fruitfully. (...) The broad range of topics covered in this book ensures that researchers/graduate students from many fields (...) will find it an invaluable guide to neural network methods.” (Prof. William W. Hsieh, University of British Columbia, Vancouver, Canada) “Vladimir Krasnopolsky has been the “founding father” of applying computation intelligence methods to environmental science; (...) Dr. Krasnopolsky has created a masterful exposition of a young, yet maturing field that promises to advance a deeper understanding of best modeling practices in environmental science.” (Dr. Sue Ellen Haupt, National Center for Atmospheric Research, Boulder, USA) “Vladimir Krasnopolsky has written an important and wonderful book on applications of neural networks to replace complex and expensive computational algorithms within Earth System Science models. He is uniquely qualified to write this book, since he has been a true pioneer with regard to many of these applications. (...) Many other examples of creative emulations will inspire not just readers interested in the Earth Sciences, but any other modeling practitioner (...) to address both theoretical and practical complex problems that may (or will!) arise in a complex system." ” (Prof. Eugenia Kalnay, University of Maryland, USA)

DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.

Advances in signal and image processing for remote sensing have been tremendous in recent years. The progress has been particularly significant with the use of deep learning based techniques to solve remote sensing problems. These advancements are the focus of this third edition of Signal and Image Processing for Remote Sensing. It emphasizes the use of machine learning approaches for the extraction of remote sensing information. Other topics include change detection in remote sensing and compressed sensing. With 19 new chapters written by world leaders in the field, this book provides an authoritative examination and offers a unique point of view on signal and image processing. Features Includes all new content and does not replace the previous edition Covers machine learning approaches in both signal and image processing for remote sensing Studies deep learning methods for remote sensing information extraction that is found in other books Explains SAR, microwave, seismic, GPR, and hyperspectral sensors and all sensors considered Discusses improved pattern classification approaches and compressed sensing approaches Provides ample examples of each aspect of both signal and image processing This book is intended for university academics, researchers, postgraduate students, industry, and government professionals who use remote sensing and its applications.

Theoretical foundations of atmospheric remote sensing are electromagnetic theory, radiative transfer and inversion theory. This book provides an overview of these topics in a common context, compile the results of recent research, as well as fill the gaps, where needed. The following aspects are covered: principles of remote sensing, the atmospheric physics, foundations of the radiative transfer theory, electromagnetic absorption, scattering and propagation, review of computational techniques in radiative transfer, retrieval techniques as well as regularization principles of inversion theory. As such, the book provides a valuable resource for those who work with remote sensing data and want to get a broad view of theoretical foundations of atmospheric remote sensing. The book will be also useful for students and researchers working in such diverse fields like inverse problems, atmospheric physics, electromagnetic theory, and radiative transfer.

Atmospheric Remote Sensing: Principles and Applications discusses the fundamental principles of atmospheric remote sensing and their applications in different research domains. Furthermore, the book covers the basic concepts of satellite remote sensing of the atmosphere, followed by Ionospheric remote sensing tools like Global Positioning System (GPS) and Very Low Frequency (VLF) wave. Sections emphasize the applications of atmospheric remote study in Ionospheric perturbation, fire detection, aerosol characteristics over land, ocean and Himalayan regions. In addition, the application of atmospheric remote sensing in disaster management like dust storms, cyclones, smoke plume, aerosol-cloud interaction, and their impact on climate change are discussed. This book is a valuable reference for students, researchers and professionals working in atmospheric science, remote sensing, and related disciplines. - Covers the fundamentals of remote sensing as applied to atmospheric science - Includes methods and applications of remote sensing technologies for atmospheric science and related disciplines in earth science - Includes full color photographs and figures that visually represent concepts discussed in the book

Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Edition explores the most up-to-date signal and image processing methods for dealing with remote sensing problems. Although most data from satellites are in image form, signal processing can contribute significantly in extracting information from remotely sensed waveforms or time series data. This book combines both, providing a unique balance between the role of signal processing and image processing. Featuring contributions from worldwide experts, this book continues to emphasize mathematical approaches. Not limited to satellite data, it also considers signals and images from hydroacoustic, seismic, microwave, and other sensors. Chapters cover important topics in signal and image processing and discuss techniques for dealing with remote sensing problems. Each chapter offers an introduction to the topic before delving into research results, making the book accessible to a broad audience. This second edition reflects the considerable advances that have occurred in the field, with 23 of 27 chapters being new or entirely rewritten. Coverage includes new mathematical developments such as compressive sensing, empirical mode decomposition, and sparse representation, as well as new component analysis methods such as non-negative matrix and tensor factorization. The book also presents new experimental results on SAR and hyperspectral image processing. The emphasis is on mathematical techniques that will far outlast the rapidly changing sensor, software, and hardware technologies. Written for industrial and academic researchers and graduate students alike, this book helps readers connect the "dots" in image and signal processing. New in This Edition The second edition includes four chapters from the first edition, plus 23 new or entirely rewritten chapters, and 190 new figures. New topics covered include: Compressive sensing The mixed pixel problem with hyperspectral images Hyperspectral image (HSI) target detection and classification based on sparse representation An ISAR technique for refocusing moving targets in SAR images Empirical mode decomposition for signal processing Feature extraction for classification of remote sensing signals and images Active learning methods in classification of remote sensing images Signal subspace identification of hyperspectral data Wavelet-based multi/hyperspectral image restoration and fusion The second edition is not intended to replace the first edition entirely and readers are encouraged to read both editions of the book for a more complete picture of signal and image processing in remote sensing. See Signal and Image Processing for Remote Sensing (CRC Press 2006).

This book contains a selection of refereed papers presented at the 6 Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment held in Florence, Italy on March 15-18, 1999. Over the last two decades, passive microwave remote sensing has made considerable progress, and has achieved significant results in the study of the Earth's surface and atmosphere. Many years of observations with ground-based and satellite-borne sensors have made an important contribution to improving our knowledge of many geophysical processes of the Earth's environment and of global changes. The evolution in microwave radiometers aboard satellites has increased steadily over recent years. At the same time, many investigations have been carried out both to improve the algorithms for the retrieval of geophysical parameters and to develop new technologies. The book is divided into four main sections: three of these are devoted to the observation of the Earth's surface and atmosphere, and the fourth, to future missions and new technologies. The first section deals with the study of sea and land surfaces, and reports recent advances in remote sensing of ocean wind, sea ice, soil moisture and vegetation biomass, including electromagnetic modelling and the assimilation of radiometric data in models of land surface processes. The following two sections are devoted to the measurement of atmospheric quantities which are of fundamental importance in climatology and meteorology, and, since they influence radio-wave propagation, they also impact on several other fields, including geodesy, navigational satellite and radioastronomy. The last section presents an overview of new technologies and plans for future missions.

This book constitutes the proceedings of the 8th International Conference on the Foundations of Augmented Cognition, AC 2014, held as part of HCI International 2014 which took place in Heraklion, Crete, Greece, in June 2014 and incorporated 14 conferences which similar thematic areas. HCII 2014 received a total of 4766 submissions, of which 1476 papers and 220 posters were accepted for publication after a careful reviewing process. These papers address the latest research and development efforts and highlight the human aspects of design and use of computing systems. The papers thoroughly cover the entire field of Human-Computer Interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The 34 papers presented in the AC 2014 proceedings are organized in topical sections named: emotional and cognitive issues in augmented cognition; machine learning for augmented cognition; augmented cognition for learning and training and augmented cognition for health and rehabilitation.

This book is a collection of the lectures, held at the International Summer School ISSAOS-2000 in L'Aquila (Italy), given by invited lecturers coming from both Europe and the USA. The goal of the book is to provide a broad panorama of spaceborne remote sensing techniques, at both microwave and visible-infrared bands and by both active and passive sensors, for the retrieval of atmospheric and oceanic parameters. A significant emphasis is given to the physical modeling background, instrument potential and limitations, inversion methods and applications. Topics on international remote sensing programs and assimilation techniques into numerical weather forecast models are also touched. The main purpose of the book is to offer to young scientists, Ph.D. or equivalent students, and to all who would like to have a broad-spectrum understanding of spaceborne remote sensing capabilities, introductory material to each remote sensing topic written by the most qualified experts in the field.