Knowledge-based Classification of Landsat Thematic Mapper Digital Imagery PDF Download
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Author: Daniel Louis Civco
Publisher:
ISBN:
Category : Landsat Thematic Mapper
Languages : en
Pages : 428
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Book Description
Author: Daniel Louis Civco
Publisher:
ISBN:
Category : Landsat Thematic Mapper
Languages : en
Pages : 428
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Book Description
Author:
Publisher:
ISBN:
Category : Astronautics in earth sciences
Languages : en
Pages : 758
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Book Description
Author: United States. Congress. House. Committee on Science, Space, and Technology. Subcommittee on Natural Resources, Agriculture Research, and Environment
Publisher:
ISBN:
Category : Landsat satellites
Languages : en
Pages : 224
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Book Description
Author: Jezching Ton
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages : 354
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Book Description
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:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 892
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Book Description
Author: Courage Kamusoko
Publisher: Springer
ISBN: 9811380120
Category : Technology & Engineering
Languages : en
Pages : 201
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Book Description
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.
Author: John A. Richards
Publisher: Springer Science & Business Media
ISBN: 3540297111
Category : Technology & Engineering
Languages : en
Pages : 455
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Book Description
Revised and enlarged to reflect new developments in the field, the fourth edition of this well-established text provides an introduction to quantitative evaluation of satellite- and aircraft-derived remotely retrieved data. Each chapter covers the pros and cons of digital remotely sensed data, without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their capabilities and limitations.
Author: Yi-huang Tao
Publisher:
ISBN:
Category : Artificial satellites in remote sensing
Languages : en
Pages : 300
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Book Description
Author: Charles W. Finkl
Publisher: Springer
ISBN: 331925121X
Category : Science
Languages : en
Pages : 297
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Book Description
This university-level reference work covers a range of remote sensing techniques that are useful for mapping and visualizing benthic environments on continental shelves. Chapters focus on overviews of the history and future of seafloor mapping techniques, cartographical visualisation and communication of seafloor mapping, and practical applications of new technologies. Seabed mapping is referenced by high-resolution seismic methods, sidescan sonar, multibeam bathymetry, satellite imagery, LiDAR, acoustic backscatter techniques, and soundscape ecology monitoring, use of autonomous underwater vehicles, among other methods. The wide breadth of subjects in this volume provides diversified coverage of seafloor imaging. This collection of modern seafloor mapping techniques summarizes the state of the art methods for mapping continental shelves.
