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Deep Learning for Remote Sensing Images with Open Source Software
Published by Taylor & Francis Ltd, 2022
ISBN 10: 0367518988 ISBN 13: 9780367518981
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Deep Learning for Remote Sensing Images with Open Source Software (Signal and Image Processing of Earth Observations)
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Deep Learning for Remote Sensing Images with Open Source Software (Signal and Image Processing of Earth Observations)
Published by Taylor & Francis Group, 2022
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Deep Learning for Remote Sensing Images with Open Source Software (Signal and Image Processing of Earth Observations)
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Deep Learning for Remote Sensing Images With Open Source Software
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Deep Learning for Remote Sensing Images with Open Source Software (Signal and Image Processing of Earth Observations)
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Deep Learning for Remote Sensing Images With Open Source Software
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Deep Learning for Remote Sensing Images with Open Source Software (Signal and Image Processing of Earth Observations)
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Deep Learning for Remote Sensing Images with Open Source Software (Signal and Image Processing of Earth Observations)
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Deep Learning for Remote Sensing Images with Open Source Software (Signal and Image Processing of Earth Observations)
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Deep Learning for Remote Sensing Images with Open Source Software
Published by Taylor & Francis Ltd, 2022
ISBN 10: 0367518988 ISBN 13: 9780367518981
Language: English
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Deep Learning for Remote Sensing Images with Open Source Software
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Deep Learning for Remote Sensing Images with Open Source Software (Signal and Image Processing of Earth Observations)
Published by Taylor & Francis Group, 2022
ISBN 10: 0367518988 ISBN 13: 9780367518981
Language: English
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Add to basketKartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Remi Cresson received the M. Sc. in electrical engineering from the Grenoble Institute of Technology, France, 2009. He is with the Land, Environment, Remote Sensing and Spatial Information Joint Research Unit (UMR TETIS), at the French Research Institute o.
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£ 75.60
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 applicationsIntroduces 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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Hardcover. Condition: Brand New. 151 pages. 9.50x6.25x0.50 inches. In Stock. This item is printed on demand.
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£ 107.70
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Remi Cresson received the M. Sc. in electrical engineering from the Grenoble Institute of Technology, France, 2009. He is with the Land, Environment, Remote Sensing and Spatial Information Joint Research Unit (UMR TETIS), at the French Research Institute o.
