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Deep learning for remote sensing images with open source software

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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Cresson, Remi (Autor)
Formato: eBook
Idioma:Inglés
Publicado: Boca Raton : CRC Press, 2020.
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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245 1 0 |a Deep learning for remote sensing images with open source software  |c Remi Cresson. 
264 1 |a Boca Raton :  |b CRC Press,  |c 2020. 
300 |a 1 online resource  |b illustrations (colour) 
520 |a 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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650 0 |a Remote-sensing images. 
650 0 |a Image processing  |x Digital techniques. 
650 0 |a Machine learning. 
650 0 |a Neural networks (Computer science) 
650 0 |a Open source software. 
650 6 |a Télédétection  |x Informatique. 
650 6 |a Images-satellite. 
650 6 |a Traitement d'images  |x Techniques numériques. 
650 6 |a Apprentissage automatique. 
650 6 |a Réseaux neuronaux (Informatique) 
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650 7 |a Machine learning  |2 fast 
650 7 |a Neural networks (Computer science)  |2 fast 
650 7 |a Open source software  |2 fast 
650 7 |a Remote sensing  |x Data processing  |2 fast 
650 7 |a Remote-sensing images  |2 fast 
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