Image Analysis in Earth Sciences is a graduate level textbook for researchers and students interested in the quantitative microstructure and texture analysis of earth materials. Methods of analysis and applications are introduced using carefully worked examples. The input images are typically derived from earth materials, acquired at a wide range of scales, through digital photography, light and electron microscopy. The book focuses on image acquisition, pre- and post-processing, on the extraction of objects (segmentation), the analysis of volumes and grain size distributions, on shape fabric analysis (particle and surface fabrics) and the analysis of the frequency domain (FFT and ACF). The last chapters are dedicated to the analysis of crystallographic fabrics and orientation imaging. Throughout the book the free software Image SXM is used.

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.

Introduces methods of data analysis in geosciences using MATLAB such as basic statistics for univariate, bivariate and multivariate datasets, jackknife and bootstrap resampling schemes, processing of digital elevation models, gridding and contouring, geostatistics and kriging, processing and georeferencing of satellite images, digitizing from the screen, linear and nonlinear time-series analysis and the application of linear time-invariant and adaptive filters. Includes a brief description of each method and numerous examples demonstrating how MATLAB can be used on data sets from earth sciences.

Possibly the greatest change confronting the practitioner and student of remote sensing in the period since the first edition of this text appeared in 1986 has been the enormous improvement in accessibility to image processing technology. Falling hardware and software costs, combined with an increase in functionality through the development of extremely versatile user interfaces, has meant that even the user unskilled in computing now has immediate and ready access to powerful and flexible means for digital image analysis and enhancement. An understanding, at algorithmic level, of the various methods for image processing has become therefore even more important in the past few years to ensure the full capability of digital image processing is utilised. This period has also been a busy one in relation to digital data supply. Several nations have become satellite data gatherers and providers, using both optical and microwave technology. Practitioners and researchers are now faced, therefore, with the need to be able to process imagery from several sensors, together with other forms of spatial data. This has been driven, to an extent, by developments in Geographic Information Systems (GIS) which, in tum, have led to the appearance of newer image processing procedures as adjuncts to more traditional approaches.

A guide to image interpretation, this book contains detailed color plates and tables that compare satellite imaging systems, list remote sensing web sites, and detail photointerpretation equipment. It includes case histories of the search for petroleum and mineral deposits and examines engineering uses of remote sensing. The volume comprises four sections: project initiation; exploration techniques; exploitation and engineering remote sensing; and environmental concerns. They combine to provide readers with a solid foundation of what image interpretation is and enables them to recognize features of interest and effectively use imagery in projects for the petroleum, mining, or groundwater industries.

This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book.

Remote Sensing Applications in Environmental and Earth System Sciences is a contemporary, multi-disciplinary, multi-scaling, updated, and upgraded approach of applied remote sensing in the environment. The book begins with an overview of remote sensing technology, and then explains the types of data that can be used as well as the image processing and analysis methods that can be applied to each type of application through the use of case studies throughout. Includes a wide spectrum of environmental applications and issues Explains methodological image analysis and interpretation procedures for conducting a variety of environmental analyses Discusses the development of early warning systems Covers monitoring of the environment as a whole – atmosphere, land, and water Explores the latest remote sensing systems in environmental applications This book is an excellent resource for anyone who is interested in remote sensing technologies and their use in Earth systems, natural resources, and environmental science.

This book brings together a collection of invited interdisciplinary persp- tives on the recent topic of Object-based Image Analysis (OBIA). Its c- st tent is based on select papers from the 1 OBIA International Conference held in Salzburg in July 2006, and is enriched by several invited chapters. All submissions have passed through a blind peer-review process resulting in what we believe is a timely volume of the highest scientific, theoretical and technical standards. The concept of OBIA first gained widespread interest within the GIScience (Geographic Information Science) community circa 2000, with the advent of the first commercial software for what was then termed ‘obje- oriented image analysis’. However, it is widely agreed that OBIA builds on older segmentation, edge-detection and classification concepts that have been used in remote sensing image analysis for several decades. Nevert- less, its emergence has provided a new critical bridge to spatial concepts applied in multiscale landscape analysis, Geographic Information Systems (GIS) and the synergy between image-objects and their radiometric char- teristics and analyses in Earth Observation data (EO).

Natural and human-induced changes in Earth's interior, land surface, biosphere, atmosphere, and oceans affect all aspects of life. Understanding these changes requires a range of observations acquired from land-, sea-, air-, and space-based platforms. To assist NASA, NOAA, and USGS in developing these tools, the NRC was asked to carry out a "decadal strategy" survey of Earth science and applications from space that would develop the key scientific questions on which to focus Earth and environmental observations in the period 2005-2015 and beyond, and present a prioritized list of space programs, missions, and supporting activities to address these questions. This report presents a vision for the Earth science program; an analysis of the existing Earth Observing System and recommendations to help restore its capabilities; an assessment of and recommendations for new observations and missions for the next decade; an examination of and recommendations for effective application of those observations; and an analysis of how best to sustain that observation and applications system.