Learning to Understand Remote Sensing Images

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Publisher : MDPI
ISBN 13 : 3038976849
Total Pages : 426 pages
Book Rating : 4.0/5 (389 download)

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Book Synopsis Learning to Understand Remote Sensing Images by : Qi Wang

Download or read book Learning to Understand Remote Sensing Images written by Qi Wang and published by MDPI. This book was released on 2019-09-30 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Learning to Understand Remote Sensing Images

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Publisher : MDPI
ISBN 13 : 3038976989
Total Pages : 376 pages
Book Rating : 4.0/5 (389 download)

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Book Synopsis Learning to Understand Remote Sensing Images by : Qi Wang

Download or read book Learning to Understand Remote Sensing Images written by Qi Wang and published by MDPI. This book was released on 2019-09-30 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images

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Publisher : MDPI
ISBN 13 : 3036509860
Total Pages : 438 pages
Book Rating : 4.0/5 (365 download)

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Book Synopsis Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images by : Yakoub Bazi

Download or read book Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images written by Yakoub Bazi and published by MDPI. This book was released on 2021-06-15 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Learning to Understand Remote Sensing Images: Volume 1

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Publisher :
ISBN 13 : 9783038976851
Total Pages : 1 pages
Book Rating : 4.9/5 (768 download)

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Book Synopsis Learning to Understand Remote Sensing Images: Volume 1 by : Qi Wang

Download or read book Learning to Understand Remote Sensing Images: Volume 1 written by Qi Wang and published by . This book was released on 2019 with total page 1 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Deep Learning for Remote Sensing Images with Open Source Software

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Author :
Publisher : Signal and Image Processing of Earth Observations
ISBN 13 : 9780367518981
Total Pages : 152 pages
Book Rating : 4.5/5 (189 download)

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Book Synopsis Deep Learning for Remote Sensing Images with Open Source Software by : Rémi Cresson

Download or read book Deep Learning for Remote Sensing Images with Open Source Software written by Rémi Cresson and published by Signal and Image Processing of Earth Observations. This book was released on 2022-01-16 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches are generic and adapted to suit applications for various remote sensing images processing in landcover mapping, forestry, urban, in disaster mapping, image restoration, etc.

Interpreting Remote Sensing Imagery

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Publisher : CRC Press
ISBN 13 : 9781420032819
Total Pages : 324 pages
Book Rating : 4.0/5 (328 download)

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Book Synopsis Interpreting Remote Sensing Imagery by : Robert R. Hoffman

Download or read book Interpreting Remote Sensing Imagery written by Robert R. Hoffman and published by CRC Press. This book was released on 2019-06-12 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Introduction to Remote Sensing, Fifth Edition

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Publisher : Guilford Press
ISBN 13 : 1609181778
Total Pages : 717 pages
Book Rating : 4.6/5 (91 download)

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Book Synopsis Introduction to Remote Sensing, Fifth Edition by : James B. Campbell

Download or read book Introduction to Remote Sensing, Fifth Edition written by James B. Campbell and published by Guilford Press. This book was released on 2011-06-15 with total page 717 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book has been replaced by Introduction to Remote Sensing, Sixth Edition, 978-1-4625-4940-5.

Deep Learning for Remote Sensing Images with Open Source Software

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Publisher : CRC Press
ISBN 13 : 100009359X
Total Pages : 165 pages
Book Rating : 4.0/5 ( download)

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Book Synopsis Deep Learning for Remote Sensing Images with Open Source Software by : Rémi Cresson

Download or read book Deep Learning for Remote Sensing Images with Open Source Software written by Rémi Cresson and published by CRC Press. This book was released on 2020-07-15 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Remote Sensing

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Publisher : Elsevier
ISBN 13 : 0080516106
Total Pages : 585 pages
Book Rating : 4.0/5 (85 download)

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Book Synopsis Remote Sensing by : Robert A. Schowengerdt

Download or read book Remote Sensing written by Robert A. Schowengerdt and published by Elsevier. This book was released on 2012-12-02 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Computer Processing of Remotely-Sensed Images

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Publisher : John Wiley & Sons
ISBN 13 : 1119956404
Total Pages : 686 pages
Book Rating : 4.1/5 (199 download)

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Book Synopsis Computer Processing of Remotely-Sensed Images by : Paul M. Mather

Download or read book Computer Processing of Remotely-Sensed Images written by Paul M. Mather and published by John Wiley & Sons. This book was released on 2011-07-28 with total page 686 pages. Available in PDF, EPUB and Kindle. Book excerpt: This fourth and full colour edition updates and expands a widely-used textbook aimed at advanced undergraduate and postgraduate students taking courses in remote sensing and GIS in Geography, Geology and Earth/Environmental Science departments. Existing material has been brought up to date and new material has been added. In particular, a new chapter, exploring the two-way links between remote sensing and environmental GIS, has been added. New and updated material includes: A website at www.wiley.com/go/mather4 that provides access to an updated and expanded version of the MIPS image processing software for Microsoft Windows, PowerPoint slideshows of the figures from each chapter, and case studies, including full data sets, Includes new chapter on Remote Sensing and Environmental GIS that provides insights into the ways in which remotely-sensed data can be used synergistically with other spatial data sets, including hydrogeological and archaeological applications, New section on image processing from a computer science perspective presented in a non-technical way, including some remarks on statistics, New material on image transforms, including the analysis of temporal change and data fusion techniques, New material on image classification including decision trees, support vector machines and independent components analysis, and Now in full colour throughout. This book provides the material required for a single semester course in Environmental Remote Sensing plus additional, more advanced, reading for students specialising in some aspect of the subject. It is written largely in non-technical language yet it provides insights into more advanced topics that some may consider too difficult for a non-mathematician to understand. The case studies available from the website are fully-documented research projects complete with original data sets. For readers who do not have access to commercial image processing software, MIPS provides a licence-free, intuitive and comprehensive alternative.

Deep Learning for the Earth Sciences

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Publisher : John Wiley & Sons
ISBN 13 : 1119646162
Total Pages : 436 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Deep Learning for the Earth Sciences by : Gustau Camps-Valls

Download or read book Deep Learning for the Earth Sciences written by Gustau Camps-Valls and published by John Wiley & Sons. This book was released on 2021-08-18 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Remote Sensing Digital Image Analysis

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Publisher : Springer Science & Business Media
ISBN 13 : 3662024624
Total Pages : 297 pages
Book Rating : 4.6/5 (62 download)

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Book Synopsis Remote Sensing Digital Image Analysis by : John A. Richards

Download or read book Remote Sensing Digital Image Analysis written by John A. Richards and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Remote Sensing Image Classification in R

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Publisher : Springer
ISBN 13 : 9811380120
Total Pages : 189 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Remote Sensing Image Classification in R by : Courage Kamusoko

Download or read book Remote Sensing Image Classification in R written by Courage Kamusoko and published by Springer. This book was released on 2019-07-24 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers an introduction to remotely sensed image processing and classification in R using machine learning algorithms. It also provides a concise and practical reference tutorial, which equips readers to immediately start using the software platform and R packages for image processing and classification. This book is divided into five chapters. Chapter 1 introduces remote sensing digital image processing in R, while chapter 2 covers pre-processing. Chapter 3 focuses on image transformation, and chapter 4 addresses image classification. Lastly, chapter 5 deals with improving image classification. R is advantageous in that it is open source software, available free of charge and includes several useful features that are not available in commercial software packages. This book benefits all undergraduate and graduate students, researchers, university teachers and other remote- sensing practitioners interested in the practical implementation of remote sensing in R.

Fundamentals of Satellite Remote Sensing

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Publisher : CRC Press
ISBN 13 : 1498728073
Total Pages : 478 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Fundamentals of Satellite Remote Sensing by : Emilio Chuvieco

Download or read book Fundamentals of Satellite Remote Sensing written by Emilio Chuvieco and published by CRC Press. This book was released on 2016-02-24 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamentals of Satellite Remote Sensing: An Environmental Approach, Second Edition is 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 co

Advances in Mapping from Remote Sensor Imagery

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Publisher : CRC Press
ISBN 13 : 1439874581
Total Pages : 465 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Advances in Mapping from Remote Sensor Imagery by : Xiaojun Yang

Download or read book Advances in Mapping from Remote Sensor Imagery written by Xiaojun Yang and published by CRC Press. This book was released on 2012-12-12 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

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Publisher :
ISBN 13 : 9783039212163
Total Pages : 1 pages
Book Rating : 4.2/5 (121 download)

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Book Synopsis Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing by : Hyung-Sup Jung

Download or read book Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing written by Hyung-Sup Jung and published by . This book was released on 2019 with total page 1 pages. Available in PDF, EPUB and Kindle. Book excerpt: As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.

Digital Analysis of Remotely Sensed Imagery

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Publisher : Mcgraw-hill
ISBN 13 : 9780071604659
Total Pages : 674 pages
Book Rating : 4.6/5 (46 download)

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Book Synopsis Digital Analysis of Remotely Sensed Imagery by : Jay Gao

Download or read book Digital Analysis of Remotely Sensed Imagery written by Jay Gao and published by Mcgraw-hill. This book was released on 2008-12-22 with total page 674 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Jay Gao’s book on the analysis of remote sensing imagery is a well-written, easy-to-read, and informative text best serving graduate students in geosciences, and practitioners in the field of digital image analysis. Although Dr. Gao states that he has targeted his book at upper-level undergraduates and lower-level postgraduate students, its rigor and depth of mathematical analysis would challenge most students without prior experience in remote sensing and college-level mathematics. The book covers a lot of ground quickly, beginning with a basic explanation of pixels, digital numbers and histograms and advancing rapidly through a description of the most well-known satellite systems to data storage formats, rectification and classification. It best serves students who have already taken an introductory course in remote sensing. Following a three-chapter description of the basics the remaining eleven chapters are dedicated to the description of the most common image processing systems and the details of the image analysis functions which can be carried out. The largest portion of the text covers classification – spectral and spatial, neural networks, decision trees and expert systems – and is an invaluable reference to anyone interested in understanding image analysis terminology and the algorithms behind these different systems. The last chapter of the text is addressed to practitioners wishing to integrate remote sensing image data with GIS and/or GPS data. The text is nicely structured so that individual chapters can easily be skipped when their content is not of interest to the reader without impairing the understanding of later chapters. "The first three chapters of the book cover introductory material that the reader should be familiar with for the most part, but also includes a very handy summary of today’s satellite systems. Chapter one addresses basic material, such as pixel DN, coordinates, feature space, histograms, and spatial, spectral, temporal and radiometric resolution normally covered in an introductory course in remote sensing. Chapter two presents a very informative and up-to-date overview of today’s satellite instruments including meteorological, oceanographic, earth resources, hyperspectral and radar instruments. Instrument and orbital parameters are presented in tabular form and make it easy to look up technical details such as spectral and spatial resolution, orbit type, repeat cycle and other instrument characteristics quickly. Written explanations are clear, readable and provide lots of interesting insight and useful tidbits of information such as potential problems and the cost of imagery. For technicians and programmers the third chapter provides details on storage formats, including descriptions of BSQ, BIL and BIP binary formats, and the most common graphics formats like GIF, TIFF and JPEG together with data compression techniques. Non-technicians can skip this chapter since image processing software will generally take care of format conversions internally without a need for understanding the nuances of each. "Chapters four will be of interest to anyone considering the purchase of image processing software, or trying to understand the differences between systems. Gao provides a useful overview of existing software – IDRISI, ERDAS Imagine, ENVI, ER Mapper, PCI, eCognition and GRASS. A brief history of each provides useful background, and a discussion of the features of each together with a comparison (also given in tabular form) is informative to anyone considering a purchase. "Chapter five can also be viewed as a stand-alone reference on rectification, but also serves as an excellent overview of the problems of dealing with mapping on a curved surface and has particular application for geographers and cartographers. It discusses the sources of geometric distortion, coordinated systems and projections, how image rectification is done – including the use of ground control points and implications for the order of transformation employed. There is a nice example showing how accuracy is influenced by the number of GCPs employed for SPOT and Landsat TM. For non-technical students the transformation mathematics can be skipped. A rather minimal section on image subsetting and mosaicking is included. Chapter six continues in much the same vein as the previous chapter, but discussing image enhancement – techniques that improve the visual quality of an image. The terms introduced here, such as density slicing, linear enhancement, stretching, and histogram equalization, will be familiar to users of image processing software and Gao provides a useful explanation of each in turn. Other application-oriented utilities such as band ratioing, vegetation indices, IHS and Tasseled Cap transformations and principal component analysis are presented in a form which is understandable to students with good mathematical grounding. "The remainder of the text deals, to a large extent, with the topic of classification. Chapter seven initially discusses elements of image interpretation, but then devotes the chapter to a detailed presentation of the most common (and affordable) of these - spectral analysis. Gao presents the different algorithms used to define spectral distance, and then devotes text to a discussion of the inner workings of unsupervised classification systems. The section on supervised classification is a very useful reference for anyone undertaking this process – describing how to set about the classification process, the differences between the different classifiers, and how to choose an appropriate one. The concepts of fuzzy logic and sub-pixels classifiers are also presented briefly. "From this point on, the text becomes much more specialized and technical and is geared towards graduate students, those carrying out research projects, and those interested in algorithmic detail. Chapter 8 is the first dealing with artificial intelligence and describes the fundamentals of neural networks. It provides sufficient information for a technically-minded non-specialist to understand the workings of such a system and serves as a good introduction to someone who is considering this field of research. Chapter nine offers an explanation of decision trees with both a descriptive verbal approach and with mathematical algorithmic detail. Chapter ten addresses spatial classifiers – in particular the analysis of texture. This chapter again leans more heavily towards mathematics and the detail is more suited to readers with a strong technical bent. Gao goes on to discuss the process of image segmentation and thence the fundamentals of object-oriented classification. There is a useful overview of two popular software packages – eCognition and Feature Analyst – together with a discussion of the strengths and weaknesses of object-based classification. Chapter eleven presents an overview of expert systems. This is an advanced field of artificial intelligence and is an ambitious undertaking to describe in fifty or so pages. It is an interesting read for someone trying to gain a superficial knowledge of the workings of such a system and the associated terminology, but for anyone wishing to work in the field, a much more in-depth coverage is necessary. "At this point, the student who was just trying to understand the basics of image processing and classification (and who skipped chapters eight through eleven) should resume reading as the last three chapters provide very helpful practical information. Chapter twelve provides a useful discussion on the methodology for assessing the accuracy of a classification and includes sources of inaccuracy and interpretation of an error matrix. It provides worked examples of accuracy assessments using simple math. This is a valuable addition to the text and presents an important process that is often overlooked in reporting classification results. Chapters thirteen and fourteen also deal with very practical matters. Chapter thirteen describes procedures for handling the analysis of temporal changes via a variety of change detection algorithms, and chapter fourteen introduces the use of GIS and GPS data in image analysis. "Dr. Gao has written an excellent text describing technical information in a very readable manner. His book will serve as a good text for a course in remote sensing/image analysis, assuming that the student has received instruction in the fundamentals of remote sensing and been introduced to some image processing software. Students wishing to become adept at the practicalities of fundamental image processing skills and classification can easily skip the mid section of the text, whereas those who are keen to learn about more sophisticated classifiers will gain the fundamentals of these from this section. Overall I found the book very informative and a pleasure to read." Reviewed by Helen M. Cox, PhD. Associate Professor, Department of Geography, California State University, Northridge