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Author: Edwin J. Medina Rivera
Publisher:
ISBN:
Category : Radar
Languages : en
Pages : 284
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Book Description
Author: Edwin J. Medina Rivera
Publisher:
ISBN:
Category : Radar
Languages : en
Pages : 284
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Book Description
Author: Elizabeth L. Simms
Publisher: CRC Press
ISBN: 0429555326
Category : Science
Languages : en
Pages : 136
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Book Description
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.
Author: James Richard Anderson
Publisher:
ISBN:
Category : Land cover
Languages : en
Pages : 36
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Book Description
Author: Chandra P. Giri
Publisher: CRC Press
ISBN: 1420070754
Category : Nature
Languages : en
Pages : 477
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Book Description
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 aro
Author: Erwin J. Villiger
Publisher:
ISBN:
Category : Image converters
Languages : en
Pages : 220
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Book Description
Investigators engaged in research utilizing remotely-sensed data are increasingly faced with a plethora of data sources and platforms that exploit different portions of the electromagnetic spectrum. Considerable efforts have focused on the application of these sources to the development of a better understanding of lithosphere, biosphere, and atmospheric systems. Many of these efforts have concentrated on the use of single sensors. More recently, some research efforts have turned to the fusion of sources in an effort to determine if different sensors and platforms can be combined to more effectively analyze or model the systems in question. This study evaluates multisensor integration of Synthetic Aperture Radar (SAR) with Multispectral Imagery (MSI) data for land cover analysis and vegetation mapping. Three principle analytical issues are addressed in this investigation: the value of SAR collected at different incident angles, preclassification processing alternatives to improve fusion classification results, and the value of cross-season (dry and wet) data integration in a subtropical climate. The study site for this research is Andros Island, the largest island in The Bahamas archipelago. Andros has a number of distinct plant communities ranging from saltwater marsh and mangroves to pine stands and hardwood coppices. Despite the island's size and proximity to the United States, it is largely uninhabited and has large expanses of minimally disturbed landscapes. An empirical assessment of SAR filtering techniques, namely speckle suppression and texture analysis at various window sizes, is utilized to determine the most appropriate technique to apply when integrating SAR and MSI for land cover characterization. Multiple RADARSAT-1 SAR images were collected at various incident angles for wet and dry season conditions over the region of interest. Two Landsat Thematic Mapper-5 MSI datasets were also collected to coincide with the time periods of the SAR images. A land cover classification process applied to the dry season and wet season MSI data achieved a total classification accuracy of 80.6% and 80.7% respectively. When combined into a single multiseason dataset the MSI data resulted in a total classification accuracy of 87.3%. SAR proved to be a valuable source of information especially when processed as a time series and with a speckle suppression algorithm applied. A 21-scene multitemporal SAR dataset achieved a total classification accuracy of 65.8%. When a classification was applied to the multitemporal dataset following speckle suppression, the resulting total classification accuracy was as high as 83.8% depending on the speckle algorithm and kernel applied. While texture measures have been successfully utilized for integrating SAR and MSI data, in this study speckle suppression proved to be significantly more valuable. SAR collection parameters such as look direction (ascending or descending orbit) and incident angle did not prove to contain uniquely valuable characteristics. The highest total classification accuracy achieved involved a combination of two MSI datasets and a multitemporal SAR dataset processed to suppress speckle using a Gamma- Maximum A Posteriori (MAP) filter with a 9x9 kernel. This study sought to investigate processing alternatives when fusing SAR and MSI data. While not all of the results met with expectations, this study does determine that SAR and MSI are complementary data sources. A combination of SAR and MSI provide unique and valuable results that can not be achieved by each variable used independently.
Author: Courage Kamusoko
Publisher: Springer Nature
ISBN: 9811651493
Category : Computers
Languages : en
Pages : 126
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Book Description
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.
Author: Rohan Bennett
Publisher:
ISBN: 9783039430543
Category :
Languages : en
Pages : 212
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Book Description
What is land? Who owns it? Who can use it? How much is it worth? What can it be used for? These are the questions land administration seeks to answer responsibly, which requires trustworthy people, transparent processes, and reliable information systems. Spatial information is an essential ingredient, and is embedded in the cadastral plans, maps, and land registry records that are used to prove ownership, trade land, access credit, resolve land disputes, enable fair taxation, and support land use planning and development. In the past, ground-based surveying techniques were used to capture the information, however, advances in remote sensing are driving the development of approaches that are faster, lower in cost, more accurate, or more participatory. These can be used to build land administration systems that better support poverty reduction, rapid urbanization, vertical development, and complex infrastructure management. The contributions contained in this book unpack these developments and the potential impacts and explore applications of high-resolution satellite imagery, unmanned aerial vehicle imagery, laser scanning, airborne and terrestrial (LiDAR), machine learning, and artificial intelligence methods, as applied to land administration in parts of Europe, Asia, and Africa.
Author: Ioannis Manakos
Publisher: Springer
ISBN: 9400779690
Category : Science
Languages : en
Pages : 436
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Book Description
Land use and land cover (LULC) as well as its changes (LUCC) are an interplay between bio-geophysical characteristics of the landscape and climate as well as the complex human interaction including its different patterns of utilization superimposed on the natural vegetation. LULC is a core information layer for a variety of scientific and administrative tasks(e.g. hydrological modelling, climate models, land use planning).In particular in the context of climate change with its impacts on socio-economic, socio-ecologic systems as well as ecosystem services precise information on LULC and LUCC are mandatory baseline datasets required over large areas. Remote sensing can provide such information on different levels of detail and in a homogeneous and reliable way. Hence, LULC mapping can be regarded as a prototype for integrated approaches based on spaceborne and airborne remote sensing techniques combined with field observations. The book provides for the first time a comprehensive view of various LULC activities focusing on European initiatives, such as the LUCAS surveys, the CORINE land covers, the ESA/EU GMES program and its resulting Fast-Track- and Downstream Services, the EU JRC Global Land Cover, the ESA GlobCover project as well as the ESA initiative on Essential Climate Variables. All have and are producing highly appreciated land cover products. The book will cover the operational approaches, but also review current state-of-the-art scientific methodologies and recommendations for this field. It opens the view with best-practice examples that lead to a view that exceeds pure mapping, but to investigate into drivers and causes as well as future projections.
Author: S. Jenicka
Publisher: Springer Nature
ISBN: 303066595X
Category : Technology & Engineering
Languages : en
Pages : 176
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Book Description
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.
Author: Prasad Thenkabail
Publisher: CRC Press
ISBN: 1482282674
Category : Technology & Engineering
Languages : en
Pages : 2304
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Book Description
A volume in the three-volume Remote Sensing Handbook series, Remote Sensing of Water Resources, Disasters, and Urban Studies documents the scientific and methodological advances that have taken place during the last 50 years. The other two volumes in the series are Remotely Sensed Data Characterization, Classification, and Accuracies, and Land Reso
