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Deep Learning for Remote Sensing Images with Open Source Software Harcover

Author

Rémi Cresson

Title

Deep Learning for Remote Sensing Images with Open Source Software Harcover

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Overview

 

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:
1) 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)
2) Presents approaches suited for real world images and data targeting large scale processing and GIS applications
3) 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)
4) Suited for deep learning beginners and readers with some GIS knowledge. No coding knowledge is required to learn practical skills.
5) Includes deep learning techniques through many step by step remote sensing data processing exercises.

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Product Details

 

ISBN-13:	9780367858483
Publisher:	Taylor & Francis
Publication date:	7/2020
Pages:	152

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Price

$99.95

Language

English

Author

Rémi Cresson

ISBN-13

9780367858483

Publisher

Taylor & Francis

Publish Time

Mon, 09/28/2020 - 12:00

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Flat rate

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10% Discount

Stock level

10

Is Paperback available?

No

Is Hardcover available?

Yes

Hardcover

$99.95

Is Ebook available?

Yes

Ebook

$99.95

Ebook Link

Ebook

Hardcover Link

Hardcover

Category

Science Education

Overview

 

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:
1) 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)
2) Presents approaches suited for real world images and data targeting large scale processing and GIS applications
3) 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)
4) Suited for deep learning beginners and readers with some GIS knowledge. No coding knowledge is required to learn practical skills.
5) Includes deep learning techniques through many step by step remote sensing data processing exercises.

Show more ▼

 

Product Details

 

ISBN-13:	9780367858483
Publisher:	Taylor & Francis
Publication date:	7/2020
Pages:	152