Deep Learning for Remote Sensing Images With Open Source Software

This is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches are generic and adapted to suit applications for various remote sensing images processing in landcover mapping, forestry, urban, in disaster mapping, image restoration, etc.

List of contents

Introduction
I Backgrounds
II Patch Based Classification
III Semantic Segmentation
IV Image Restoration

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).

Summary

This is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches are generic and adapted to suit applications for various remote sensing images processing in landcover mapping, forestry, urban, in disaster mapping, image restoration, etc.

Product details

• Authors: Rémi Cresson, Remi (Irstea/inrae/umr Tetis) Cresson
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 31.07.2020
• EAN: 9780367858483
• ISBN: 978-0-367-85848-3
• No. of pages: 152
• Series: Signal and Image Processing of Earth Observations
• Subjects: Natural sciences, medicine, IT, technology > Technology > Miscellaneous; TECHNOLOGY & ENGINEERING / Environmental / General, TECHNOLOGY & ENGINEERING / Imaging Systems, COMPUTERS / Image Processing, COMPUTERS / Machine Theory, environmental science, engineering & technology, Urban & municipal planning, Earth Sciences, Forestry & silviculture: practice & techniques, Agricultural science, Image processing, Urban and municipal planning and policy, Environmental science, engineering and technology, Risk assessment, Geographical information systems (GIS) & remote sensing, Geographical information systems, geodata and remote sensing, Forestry and silviculture