Deep Learning for Remote Sensing Images with Open Source Software: 1st Edition (Hardback) book cover

Deep Learning for Remote Sensing Images with Open Source Software

1st Edition

By Rémi Cresson

CRC Press

158 pages | 68 Color Illus.

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Hardback: 9780367858483
pub: 2020-06-17
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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.

Table of Contents

Introduction. Deep Learning Backgrounds. Patch Based Image Classification Using CNN. Patch Based Image Classification Using FCNN. Patch Based Image Classification with Hybrid Classifiers. Using Multiple Sources with Different Modalities. In Deep Learning Architectures. Semantic Segmentation of High-Resolution Images. Using a Convolutional Autoencoder Architecture to Perform Cloud Removal in Optical Image. Conclusions.

About the Author

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 of Science and Technology for Environment and Agriculture (Irstea), Montpellier, France. His research and engineering interests include remote sensing image processing, High Performance Computing, and geospatial data inter-operability. He is member of the Orfeo ToolBox Project Steering Committee and charter member of the Open source geospatial foundation (OSGEO).

About the Series

Signal and Image Processing of Earth Observations

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Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Image Processing
COMPUTERS / Machine Theory
TECHNOLOGY & ENGINEERING / Environmental / General
TECHNOLOGY & ENGINEERING / Remote Sensing & Geographic Information Systems