Download or read book Soft Classification and Land Cover Mapping from Remotely Sensed Imagery written by Huong Thi Xuan Doan and published by . This book was released on 2007 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Remote Sensing of Land Use and Land Cover written by Chandra P. Giri and published by CRC Press. This book was released on 2012-05-02 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book A Land Use and Land Cover Classification System for Use with Remote Sensor Data written by James Richard Anderson and published by . This book was released on 1976 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Historical Land Use Land Cover Classification Using Remote Sensing written by Wafi Al-Fares and published by Springer Science & Business Media. This book was released on 2013-06-25 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Land Cover Classification of Remotely Sensed Images written by S. Jenicka and published by Springer Nature. This book was released on 2021-03-10 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Classification Methods for Remotely Sensed Data written by Paul Mather and published by CRC Press. This book was released on 2001-12-06 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Remote sensing is an integral part of geography, GIS and cartography, used by academics in the field and professionals in all sorts of occupations. The 1990s saw the development of a range of new methods of classifying remote sensing images and data, both optical imaging and microwave imaging. This comprehensive survey of the various techniques pul
Download or read book Uncertainty in Remote Sensing and GIS written by Giles M. Foody and published by John Wiley & Sons. This book was released on 2003-07-11 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: Remote sensing and geographical information science (GIS) have advanced considerably in recent years. However, the potential of remote sensing and GIS within the environmental sciences is limited by uncertainty, especially in connection with the data sets and methods used. In many studies, the issue of uncertainty has been incompletely addressed. The situation has arisen in part from a lack of appreciation of uncertainty and the problems it can cause as well as of the techniques that may be used to accommodate it. This book provides general overviews on uncertainty in remote sensing and GIS that illustrate the range of uncertainties that may occur, in addition to describing the means of measuring uncertainty and the impacts of uncertainty on analyses and interpretations made. Uncertainty in Remote Sensing and GIS provides readers with comprehensive coverage of this largely undocumented subject: * Relevant to a broad variety of disciplines including geography, environmental science, electrical engineering and statistics * Covers range of material from base overviews to specific applications * Focuses on issues connected with uncertainty at various points along typical data analysis chains used in remote sensing and GIS Written by an international team of researchers drawn from a variety of disciplines, Uncertainty in Remote Sensing and GIS provides focussed discussions on topics of considerable importance to a broad research and user community. The book is invaluable reading for researchers, advanced students and practitioners who want to understand the nature of uncertainty in remote sensing and GIS, its limitations and methods of accommodating it.
Download or read book Guidelines for the Use of Digital Imagery for Vegetation Mapping written by Henry Lachowski and published by DIANE Publishing. This book was released on 1996-09 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: A basic reference for those considering digital imagery, particularly satellite imagery for vegetation mapping. Contents: using remote sensing and GIS for mapping vegetation; remote sensors and remotely sensed data; determining appropriate uses for satellite imagery; defining the classification scheme; collecting reference data; assessing accuracy; creating polygons; project management; the basic tour; and case studies. Important terms and ideas are introduced while showing the progression of key activities in the classification and mapping process.
Download or read book Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification written by Anil Kumar and published by CRC Press. This book was released on 2020-07-19 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the state-of-art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance, forest fire mapping, stubble burning, post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well, this book addresses other related algorithms based on distance, kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels, non-linearity and noisy pixels. Further, this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate, postgraduate, research scholars and working professionals of different branches such as Geoinformation sciences, Geography, Electrical, Electronics and Computer Sciences etc., working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields, for example, in medical imaging. Overall, this book aims to: exclusive focus on using large range of fuzzy classification algorithms for remote sensing images; discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images; describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms; explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool, Entropy, Correlation Coefficient, Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and; combines explanation of the algorithms with case studies and practical applications.
Download or read book Guidelines for the Use of Digital Imagery for Vegetation Mapping written by and published by . This book was released on 1995 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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.
Download or read book Development and Evaluation of Advanced Classification Systems Using Remotely Sensed Data for Accurate Land use land cover Mapping written by Hui Yuan and published by . This book was released on 2002 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keywords: land-use/land-cover, GIS, classification, remote sensing, mapping.
Download or read book Integrated Use of Remotely Sensed and Existing Data Sources with Man machine Interaction to Achieve Level II Land Use and Land Cover Classification written by Wayne L. Myers and published by . This book was released on 1987 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Remote Sensing Image Analysis Including the Spatial Domain written by Steven M. de Jong and published by Springer Science & Business Media. This book was released on 2007-07-26 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Remote Sensing of Land Use and Land Cover written by Chandra P Giri and published by CRC Press. This book was released on 2020-10-02 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book An Evaluation of Remotely Sensed Images and Machine Learning Algorithms for Accurate Mapping of Fine scale Landscape Patterns in the Eastern USA written by Vishruta Prashant Yawatkar and published by . This book was released on 2021 with total page 69 pages. Available in PDF, EPUB and Kindle. Book excerpt: NAIP imagery is not collected as frequently as satellite imagery such as Sentinel-2, and thus it cannot provide frequently generated maps. As a step towards being able to generate frequent land cover maps on a local system, I used the high temporal and spectral resolution Sentinel-2 satellite remote sensing images on a high-performance computing platform. These images were used to classify the land cover into seven very different classes with an overall accuracy of 85.6% and 86.5% using a random forest and a neural network algorithm, respectively. The information generated from this part of the analysis is a vital step towards a future goal of using a spectral unmixing method for classifying the same Sentinel-2 satellite remote sensing images at the sub-pixel level. Overall, the information generated by this study is vital to identify specific land cover areas such as areas with the highest saltwater intrusion impact and to inform agricultural land management and policy measures in the region.
Download or read book Subpixel Mapping for Remote Sensing Images written by Peng Wang and published by CRC Press. This book was released on 2022-12-15 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: Subpixel mapping is a technology that generates a fine resolution land cover map from coarse resolution fractional images by predicting the spatial locations of different land cover classes at the subpixel scale. This book provides readers with a complete overview of subpixel image processing methods, basic principles, and different subpixel mapping techniques based on single or multi-shift remote sensing images. Step-by-step procedures, experimental contents, and result analyses are explained clearly at the end of each chapter. Real-life applications are a great resource for understanding how and where to use subpixel mapping when dealing with different remote sensing imaging data. This book will be of interest to undergraduate and graduate students, majoring in remote sensing, surveying, mapping, and signal and information processing in universities and colleges, and it can also be used by professionals and researchers at different levels in related fields.
