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Books > Computing & IT > Applications of computing > Image processing
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Land Cover Classification of Remotely Sensed Images - A Textural Approach (Paperback, 1st ed. 2021)
Loot Price: R3,802
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Land Cover Classification of Remotely Sensed Images - A Textural Approach (Paperback, 1st ed. 2021)
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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.
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