Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification

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, Eectrical, 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: <ul> <li>exclusive focus on using large range of fuzzy classification algorithms for remote sensing images;</li> <li>discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images;</li> <li>describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms;</li> <li>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;</li> <li>combines explanation of the algorithms with case studies and practical applications.</li> </ul>

Computers & Technology

Kumar, A Senthil

Kumar, Anil.

Upadhyay, Priyadarshi

Computer Science

CRC-Press

Fixtures are an essential part of manufacturing production. This book covers computer-aided fixture design, fixture clamping synthesis and optimisation, workpiece-fixture interaction, intelligent fixture designed to integrate with processing equipment or machine tools so as to improve productivity and product quality, Internet-enabled fixture design and modular fixture database management. These are the emerging issues central to the development of computer-integrated manufacturing.Covering the established knowledge of fixture design automation and the niche areas of fixture system integration and Internet-enabled design, the book would be a prevalent reference for academics, manufacturing &amp; industrial engineers, and a valuable text for engineering graduate students.

ADVANCED TREATISE ON FIXTURE DESIGN AND PLANNING, AN (Manufacturing Systems and Technology)

Nee, Andrew Yeh Ching

Tao, Zhen Jun

Graphics & Design

World Scientific Publishing Company

See the best price to sell, buy, or rent books by Kumar, A Senthil. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Kumar, A Senthil