Land Cover Classification Of Remotely Sensed Images
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Land Cover Classification Of Remotely Sensed Images
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Author : S. Jenicka
language : en
Publisher: Springer Nature
Release Date : 2021-03-10
Land Cover Classification Of Remotely Sensed Images written by S. Jenicka and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-10 with Technology & Engineering categories.
The book introduces two domains namely Remote Sensing and Digital Image Processing. It discusses remote sensing, texture, classifiers, and procedures for performing the texture-based segmentation and land cover classification. The first chapter discusses the important terminologies in remote sensing, basics of land cover classification, types of remotely sensed images and their characteristics. The second chapter introduces the texture and a detailed literature survey citing papers related to texture analysis and image processing. The third chapter describes basic texture models for gray level images and multivariate texture models for color or remotely sensed images with relevant Matlab source codes. The fourth chapter focuses on texture-based classification and texture-based segmentation. The Matlab source codes for performing supervised texture based segmentation using basic texture models and minimum distance classifier are listed. The fifth chapter describes supervised and unsupervised classifiers. The experimental results obtained using a basic texture model (Uniform Local Binary Pattern) with the classifiers described earlier are discussed through the relevant Matlab source codes. The sixth chapter describes land cover classification procedure using multivariate (statistical and spectral) texture models and minimum distance classifier with Matlab source codes. A few performance metrics are also explained. The seventh chapter explains how texture based segmentation and land cover classification are performed using the hidden Markov model with relevant Matlab source codes. The eighth chapter gives an overview of spatial data analysis and other existing land cover classification methods. The ninth chapter addresses the research issues and challenges associated with land cover classification using textural approaches. This book is useful for undergraduates in Computer Science and Civil Engineering and postgraduates who plan to do research or project work in digital image processing. The book can serve as a guide to those who narrow down their research to processing remotely sensed images. It addresses a wide range of texture models and classifiers. The book not only guides but aids the reader in implementing the concepts through the Matlab source codes listed. In short, the book will be a valuable resource for growing academicians to gain expertise in their area of specialization and students who aim at gaining in-depth knowledge through practical implementations. The exercises given under texture based segmentation (excluding land cover classification exercises) can serve as lab exercises for the undergraduate students who learn texture based image processing.
Land Cover Classification Of Remotely Sensed Images
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Author : S. Jenicka
language : en
Publisher: Springer
Release Date : 2022-03-11
Land Cover Classification Of Remotely Sensed Images written by S. Jenicka and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-11 with Technology & Engineering categories.
The book introduces two domains namely Remote Sensing and Digital Image Processing. It discusses remote sensing, texture, classifiers, and procedures for performing the texture-based segmentation and land cover classification. The first chapter discusses the important terminologies in remote sensing, basics of land cover classification, types of remotely sensed images and their characteristics. The second chapter introduces the texture and a detailed literature survey citing papers related to texture analysis and image processing. The third chapter describes basic texture models for gray level images and multivariate texture models for color or remotely sensed images with relevant Matlab source codes. The fourth chapter focuses on texture-based classification and texture-based segmentation. The Matlab source codes for performing supervised texture based segmentation using basic texture models and minimum distance classifier are listed. The fifth chapter describes supervised and unsupervised classifiers. The experimental results obtained using a basic texture model (Uniform Local Binary Pattern) with the classifiers described earlier are discussed through the relevant Matlab source codes. The sixth chapter describes land cover classification procedure using multivariate (statistical and spectral) texture models and minimum distance classifier with Matlab source codes. A few performance metrics are also explained. The seventh chapter explains how texture based segmentation and land cover classification are performed using the hidden Markov model with relevant Matlab source codes. The eighth chapter gives an overview of spatial data analysis and other existing land cover classification methods. The ninth chapter addresses the research issues and challenges associated with land cover classification using textural approaches. This book is useful for undergraduates in Computer Science and Civil Engineering and postgraduates who plan to do research or project work in digital image processing. The book can serve as a guide to those who narrow down their research to processing remotely sensed images. It addresses a wide range of texture models and classifiers. The book not only guides but aids the reader in implementing the concepts through the Matlab source codes listed. In short, the book will be a valuable resource for growing academicians to gain expertise in their area of specialization and students who aim at gaining in-depth knowledge through practical implementations. The exercises given under texture based segmentation (excluding land cover classification exercises) can serve as lab exercises for the undergraduate students who learn texture based image processing.
Historical Land Use Land Cover Classification Using Remote Sensing
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Author : Wafi Al-Fares
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-25
Historical Land Use Land Cover Classification Using Remote Sensing written by Wafi Al-Fares 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 2013-06-25 with Science categories.
Although the development of remote sensing techniques focuses greatly on construction of new sensors with higher spatial and spectral resolution, it is advisable to also use data of older sensors (especially, the LANDSAT-mission) when the historical mapping of land use/land cover and monitoring of their dynamics are needed. Using data from LANDSAT missions as well as from Terra (ASTER) Sensors, the authors shows in his book maps of historical land cover changes with a focus on agricultural irrigation projects. The kernel of this study was whether, how and to what extent applying the various remotely sensed data that were used here, would be an effective approach to classify the historical and current land use/land cover, to monitor the dynamics of land use/land cover during the last four decades, to map the development of the irrigation areas, and to classify the major strategic winter- and summer-irrigated agricultural crops in the study area of the Euphrates River Basin.
Remote Sensing Of Land Use And Land Cover
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Author : Chandra P. Giri
language : en
Publisher: CRC Press
Release Date : 2012-05-02
Remote Sensing Of Land Use And Land Cover written by Chandra P. Giri and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-05-02 with Technology & Engineering categories.
Filling the need for a comprehensive book that covers both theory and application, Remote Sensing of Land Use and Land Cover: Principles and Applications provides a synopsis of how remote sensing can be used for land-cover characterization, mapping, and monitoring from the local to the global scale. With contributions by leading scientists from around the world, this well-structured volume offers an international perspective on the science, technologies, applications, and future needs of remote sensing of land cover and land use. After an overview of the key concepts and history of land-use and land-cover mapping, the book discusses the relationship between land cover and land use and addresses the land-cover classification system. It then presents state-of-the-art methods and techniques in data acquisition, preprocessing, image interpretation, and accuracy assessment for land-use and land-cover characterization and mapping. Case studies from around the world illustrate land-cover applications at global, continental, and national scales. These examples use multiple data sources and provide in-depth understanding of land cover and land-cover dynamics in multiple spatial, thematic, and temporal resolutions. Looking to the future, the book also identifies new frontiers in land-cover mapping and forecasting. The availability and accessibility of accurate and timely land-cover data sets play an important role in many global change studies, highlighting the need for better land-use and land-cover change information at multiple scales. A synthesis of current knowledge in remote sensing of land-use and land-cover science, this book promotes more effective use of Earth observation data and technology to assess, monitor, and manage land resources.
Remote Sensing Image Analysis Including The Spatial Domain
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Author : Steven M. de Jong
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-07-26
Remote Sensing Image Analysis Including The Spatial Domain written by Steven M. de Jong 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 2007-07-26 with Science categories.
Remote Sensing image analysis is mostly done using only spectral information on a pixel by pixel basis. Information captured in neighbouring cells, or information about patterns surrounding the pixel of interest often provides useful supplementary information. This book presents a wide range of innovative and advanced image processing methods for including spatial information, captured by neighbouring pixels in remotely sensed images, to improve image interpretation or image classification. Presented methods include different types of variogram analysis, various methods for texture quantification, smart kernel operators, pattern recognition techniques, image segmentation methods, sub-pixel methods, wavelets and advanced spectral mixture analysis techniques. Apart from explaining the working methods in detail a wide range of applications is presented covering land cover and land use mapping, environmental applications such as heavy metal pollution, urban mapping and geological applications to detect hydrocarbon seeps. The book is meant for professionals, PhD students and graduates who use remote sensing image analysis, image interpretation and image classification in their work related to disciplines such as geography, geology, botany, ecology, forestry, cartography, soil science, engineering and urban and regional planning.
Remote Sensing Image Classification In R
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Author : Courage Kamusoko
language : en
Publisher: Springer
Release Date : 2019-07-24
Remote Sensing Image Classification In R written by Courage Kamusoko and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-24 with Technology & Engineering categories.
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.
Sar Image Interpretation For Various Land Covers
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Author : Elizabeth L. Simms
language : en
Publisher: CRC Press
Release Date : 2019-12-06
Sar Image Interpretation For Various Land Covers written by Elizabeth L. Simms and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-06 with Science categories.
This full color book is a comprehensive visual reference for the interpretation of synthetic aperture radar (SAR) images with examples of how technological specifications may affect interpretation solutions. It contains a summary review of image acquisition parameters of consequence on the visual representation of objects, introduces traditional interpretation keys under different light and applies them for considering regional landscape components and identifying large-scale geographical ensembles. Through elements of interpretation such as the construct of tone, texture, pattern, size, and shape, the book explains the rich unique context of many terrains. It provides also several SAR X- and C-band image examples of regional and large-scale land use and land cover (LULC) ensembles, includes important explanations for each illustration, and highlights selected SAR image applications. Ancillary information includes acquisition specifications, a geographic scale, and the image-center latitude and longitude. Features: Provides ready access to any type of information for an image interpretation problem related to current LULC classification schemes. Presents scalable geographic information interpreted at a regional scale and land cover ensembles that can also be interpreted locally. Provides comparative examples of images acquired from X- and C-band, opposed look directions, near- and far-range incidence angles, like- and cross-polarization modes. Includes practical explanations easily transferred to individual’s research projects. Designed as "visual dictionary," SAR Image Interpretation for Various Land Covers: A Practical Guide, is an excellent introduction to the visual interpretation of SAR images for numerous types of LULC. Both practitioners and students will familiarize themselves with and expand their knowledge of geographic information conveyed from radar images while government agencies and businesses that use LULC-related data for emergency response cases of for urban and regional planning, will find this book invaluable.
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.
Classification Of Remotely Sensed Images
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Author : I.L Thomas
language : en
Publisher: CRC Press
Release Date : 1987-01-01
Classification Of Remotely Sensed Images written by I.L Thomas and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987-01-01 with Science categories.
Remotely sensed images are an indispensable tool to researchers in disciplines such as cartography, forestry and geology in which mapping by satellite or aircraft is often a far more acurate, efficient and cost-effective method than conventional survey. Most users of remote sensing data are now familiar with using photographic images prepared to standard prescriptions by others to relate colours or grey tones to what actually exists on the ground. This book is aimed at the discipline-oriented user who wishes to move away from these traditional photographic interpretation methods towards interactive analysis systems. Such systems permit the classification of the digital data in ways that enable locations and quantitative information on specific themes to be extracted and portrayed. The empasis is upon the integration of these new techniques into existing discipline skills: the user is not expected to become a 'computer operator'. The book covers the fundamental theory of image classification, and illustrates it with practical examples in the form of a structured outline of a total research project. It is therefore an essential reference manual to 'sit at the elbow' of the user working with an image processing system for remotely sensed data.
Optical And Sar Remote Sensing Of Urban Areas
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Author : Courage Kamusoko
language : en
Publisher: Springer Nature
Release Date : 2021-12-02
Optical And Sar Remote Sensing Of Urban Areas written by Courage Kamusoko and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-02 with Computers categories.
This book introduces remotely sensed image processing for urban areas using optical and synthetic aperture radar (SAR) data and assists students, researchers, and remote sensing practitioners who are interested in land cover mapping using such data. There are many introductory and advanced books on optical and SAR remote sensing image processing, but most of them do not serve as good practical guides. However, this book is designed as a practical guide and a hands-on workbook, where users can explore data and methods to improve their land cover mapping skills for urban areas. Although there are many freely available earth observation data, the focus is on land cover mapping using Sentinel-1 C-band SAR and Sentinel-2 data. All remotely sensed image processing and classification procedures are based on open-source software applications such QGIS and R as well as cloud-based platforms such as Google Earth Engine (GEE). The book is organized into six chapters. Chapter 1 introduces geospatial machine learning, and Chapter 2 covers exploratory image analysis and transformation. Chapters 3 and 4 focus on mapping urban land cover using multi-seasonal Sentinel-2 imagery and multi-seasonal Sentinel-1 imagery, respectively. Chapter 5 discusses mapping urban land cover using multi-seasonal Sentinel-1 and Sentinel-2 imagery as well as other derived data such as spectral and texture indices. Chapter 6 concludes the book with land cover classification accuracy assessment.