|
Showing 1 - 3 of
3 matches in All Departments
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.
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.
Fixtures are crucial to new manufacturing techniques and largely
dictate the level of flexibility a manufacturing system can
achieve. Advanced Fixture Design for FMS provides a systematic
basis for the selection and design of fixturing systems. It gives a
review of the current state of the art of flexible and
reconfigurable fixturing systems. Recent developments in design
methodology using CAD are analysed in depth. Fixture design is seen
as an inseparable part of process planning. The primary objective
of a fixture system is to ensure that the part being manufactured
can be made consistently within the tolerance specified in the
design. A new method of tolerance analysis is used to check the
suitability of location surfaces and the sequence of operations and
is explained in detail.
|
You may like...
Barbie
Margot Robbie, Ryan Gosling, …
DVD
R194
Discovery Miles 1 940
|