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Books > Professional & Technical > Energy technology & engineering > Electrical engineering
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Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing (Hardcover)
Loot Price: R6,335
Discovery Miles 63 350
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Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing (Hardcover)
Expected to ship within 12 - 19 working days
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In the last few years the scientific community has realized that
obtaining a better understanding of interactions between natural
systems and the man-made environment across different scales
demands more research efforts in remote sensing. An integrated
Earth system observatory that merges surface-based, air-borne,
space-borne, and even underground sensors with comprehensive and
predictive capabilities indicates promise for revolutionizing the
study of global water, energy, and carbon cycles as well as land
use and land cover changes. The aim of this book is to present a
suite of relevant concepts, tools, and methods of integrated
multisensor data fusion and machine learning technologies to
promote environmental sustainability. The process of machine
learning for intelligent feature extraction consists of regular,
deep, and fast learning algorithms. The niche for integrating data
fusion and machine learning for remote sensing rests upon the
creation of a new scientific architecture in remote sensing science
that is designed to support numerical as well as symbolic feature
extraction managed by several cognitively oriented machine learning
tasks at finer scales. By grouping a suite of satellites with
similar nature in platform design, data merging may come to help
for cloudy pixel reconstruction over the space domain or
concatenation of time series images over the time domain, or even
both simultaneously. Organized in 5 parts, from Fundamental
Principles of Remote Sensing; Feature Extraction for Remote
Sensing; Image and Data Fusion for Remote Sensing; Integrated Data
Merging, Data Reconstruction, Data Fusion, and Machine Learning; to
Remote Sensing for Environmental Decision Analysis, the book will
be a useful reference for graduate students, academic scholars, and
working professionals who are involved in the study of Earth
systems and the environment for a sustainable future. The new
knowledge in this book can be applied successfully in many areas of
environmental science and engineering.
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