Books > Computing & IT > Applications of computing > Image processing
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Deep Learning for Remote Sensing Images with Open Source Software (Hardcover)
Loot Price: R2,477
Discovery Miles 24 770
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Deep Learning for Remote Sensing Images with Open Source Software (Hardcover)
Series: Signal and Image Processing of Earth Observations
Expected to ship within 12 - 17 working days
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In today's world, deep learning source codes and a plethora of open
access geospatial images are readily available and easily
accessible. However, most people are missing the educational tools
to make use of this resource. Deep Learning for Remote Sensing
Images with Open Source Software is the first practical book to
introduce deep learning techniques using free open source tools for
processing real world remote sensing images. The approaches
detailed in this book are generic and can be adapted to suit many
different applications for remote sensing image processing,
including landcover mapping, forestry, urban studies, disaster
mapping, image restoration, etc. Written with practitioners and
students in mind, this book helps link together the theory and
practical use of existing tools and data to apply deep learning
techniques on remote sensing images and data. Specific Features of
this Book: The first book that explains how to apply deep learning
techniques to public, free available data (Spot-7 and Sentinel-2
images, OpenStreetMap vector data), using open source software
(QGIS, Orfeo ToolBox, TensorFlow) Presents approaches suited for
real world images and data targeting large scale processing and GIS
applications Introduces state of the art deep learning architecture
families that can be applied to remote sensing world, mainly for
landcover mapping, but also for generic approaches (e.g. image
restoration) Suited for deep learning beginners and readers with
some GIS knowledge. No coding knowledge is required to learn
practical skills. Includes deep learning techniques through many
step by step remote sensing data processing exercises.
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