Learning To Understand Remote Sensing Images
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| Author | : Yakoub Bazi |
| Publisher | : MDPI |
| Total Pages | : 438 |
| Release | : 2021-06-15 |
| Genre | : Science |
| ISBN | : 3036509860 |
The rapid growth of the world population has resulted in an exponential expansion of both urban and agricultural areas. Identifying and managing such earthly changes in an automatic way poses a worth-addressing challenge, in which remote sensing technology can have a fundamental role to answer—at least partially—such demands. The recent advent of cutting-edge processing facilities has fostered the adoption of deep learning architectures owing to their generalization capabilities. In this respect, it seems evident that the pace of deep learning in the remote sensing domain remains somewhat lagging behind that of its computer vision counterpart. This is due to the scarce availability of ground truth information in comparison with other computer vision domains. In this book, we aim at advancing the state of the art in linking deep learning methodologies with remote sensing image processing by collecting 20 contributions from different worldwide scientists and laboratories. The book presents a wide range of methodological advancements in the deep learning field that come with different applications in the remote sensing landscape such as wildfire and postdisaster damage detection, urban forest mapping, vine disease and pavement marking detection, desert road mapping, road and building outline extraction, vehicle and vessel detection, water identification, and text-to-image matching.
| Author | : Qi Wang |
| Publisher | : MDPI |
| Total Pages | : 376 |
| Release | : 2019-09-30 |
| Genre | : Computers |
| ISBN | : 3038976989 |
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.
| Author | : Rémi Cresson |
| Publisher | : CRC Press |
| Total Pages | : 158 |
| Release | : 2020-07-15 |
| Genre | : Technology & Engineering |
| ISBN | : 1000093611 |
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.
| Author | : James B. Campbell |
| Publisher | : Guilford Press |
| Total Pages | : 717 |
| Release | : 2011-06-15 |
| Genre | : Science |
| ISBN | : 1609181778 |
This book has been replaced by Introduction to Remote Sensing, Sixth Edition, 978-1-4625-4940-5.
| Author | : John A. Richards |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 376 |
| Release | : 2009-10-08 |
| Genre | : Technology & Engineering |
| ISBN | : 3642020208 |
This book is concerned with remote sensing based on the technology of imaging radar. It assumes no prior knowledge of radar on the part of the reader, commencing with a treatment of the essential concepts of microwave imaging and progressing through to the development of multipolarisation and interferometric radar, modes which underpin contemporary applications of the technology. The use of radar for imaging the earth’s surface and its resources is not recent. Aircraft-based microwave systems were operating in the 1960s, ahead of optical systems that image in the visible and infrared regions of the spectrum. Optical remote sensing was given a strong impetus with the launch of the first of the Landsat series of satellites in the mid 1970s. Although the Seasat satellite launched in the same era (1978) carried an imaging radar, it operated only for about 12 months and there were not nearly so many microwave systems as optical platforms in service during the 1980s. As a result, the remote sensing community globally tended to develop strongly around optical imaging until Shuttle missions in the early to mid 1980s and free-flying imaging radar satellites in the early to mid 1990s became available, along with several sophisticated aircraft platforms. Since then, and particularly with the unique capabilities and flexibility of imaging radar, there has been an enormous surge of interest in microwave imaging technology. Unlike optical imaging, understanding the theoretical underpinnings of imaging radar can be challenging, particularly when new to the field.
| Author | : Robert R. Hoffman |
| Publisher | : CRC Press |
| Total Pages | : 324 |
| Release | : 2019-06-12 |
| Genre | : Technology & Engineering |
| ISBN | : 9781420032819 |
No matter how advanced the technology, there is always the human factor involved - the power behind the technology. Interpreting Remote Sensing Imagery: Human Factors draws together leading psychologists, remote sensing scientists, and government and industry scientists to consider the factors involved in expertise and perceptual skill. This boo
| Author | : Xiaojun Yang |
| Publisher | : CRC Press |
| Total Pages | : 465 |
| Release | : 2012-12-12 |
| Genre | : Technology & Engineering |
| ISBN | : 1439874581 |
Advances in Mapping from Remote Sensor Imagery: Techniques and Applications reviews some of the latest developments in remote sensing and information extraction techniques applicable to topographic and thematic mapping. Providing an interdisciplinary perspective, leading experts from around the world have contributed chapters examining state-of-the-art techniques as well as widely used methods. The book covers a broad range of topics including photogrammetric mapping and LiDAR remote sensing for generating high quality topographic products, global digital elevation models, current methods for shoreline mapping, and the identification and classification of residential buildings. Contributors also showcase cutting-edge developments for environmental and ecological mapping, including assessment of urbanization patterns, mapping vegetation cover, monitoring invasive species, and mapping marine oil spills—crucial for monitoring this significant environmental hazard. The authors exemplify the information presented in this text with case studies from around the world. Examples include: Envisat/ERS-2 images used to generate digital elevation models over northern Alaska In situ radiometric observations and MERIS images employed to retrieve chlorophyll a concentration in inland waters in Australia ERS-1/2 SAR images utilized to map spatiotemporal deformation in the southwestern United States Aerospace sensors and related information extraction techniques that support various mapping applications have recently garnered more attention due to the advances in remote sensing theories and technologies. This book brings together top researchers in the field, providing a state-of-the-art review of some of the latest advancements in remote sensing and mapping technologies.
| Author | : Cem Ünsalan |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 189 |
| Release | : 2011-05-18 |
| Genre | : Computers |
| ISBN | : 0857296671 |
This book presents a comprehensive review of image processing methods, for the analysis of land use in residential areas. Combining a theoretical framework with highly practical applications, the book describes a system for the effective detection of single houses and streets in very high resolution. Topics and features: with a Foreword by Prof. Dr. Peter Reinartz of the German Aerospace Center; provides end-of-chapter summaries and review questions; presents a detailed review on remote sensing satellites; examines the multispectral information that can be obtained from satellite images, with a focus on vegetation and shadow-water indices; investigates methods for land-use classification, introducing precise graph theoretical measures over panchromatic images; addresses the problem of detecting residential regions; describes a house and street network-detection subsystem; concludes with a summary of the key ideas covered in the book.
| Author | : John A. Richards |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 297 |
| Release | : 2013-04-17 |
| Genre | : Technology & Engineering |
| ISBN | : 3662024624 |
With the widespread availability of satellite and aircraft remote sensing image data in digital form, and the ready access most remote sensing practitioners have to computing systems for image interpretation, there is a need to draw together the range of digital image processing procedures and methodologies commonly used in this field into a single treatment. It is the intention of this book to provide such a function, at a level meaningful to the non-specialist digital image analyst, but in sufficient detail that algorithm limitations, alternative procedures and current trends can be appreciated. Often the applications specialist in remote sensing wishing to make use of digital processing procedures has had to depend upon either the mathematically detailed treatments of image processing found in the electrical engineering and computer science literature, or the sometimes necessarily superficial treatments given in general texts on remote sensing. This book seeks to redress that situation. Both image enhancement and classification techniques are covered making the material relevant in those applications in which photointerpretation is used for information extraction and in those wherein information is obtained by classification.
| Author | : Robert A. Schowengerdt |
| Publisher | : Elsevier |
| Total Pages | : 585 |
| Release | : 2012-12-02 |
| Genre | : Technology & Engineering |
| ISBN | : 0080516106 |
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.
