Deep Learning for Remote Sensing Images with Open Source Software Signal and Image Processing of Earth Observations Series
Auteur : Cresson Rémi
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.
Introduction
I Backgrounds
II Patch Based Classification
III Semantic Segmentation
IV Image Restoration
Date de parution : 01-2022
15.6x23.4 cm
Date de parution : 07-2020
15.6x23.4 cm
Disponible chez l'éditeur (délai d'approvisionnement : 14 jours).
Prix indicatif 103,03 €
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Mots-clés :
Remote Sensing Images; Open Source Software; Machine Learning; OTB; Image Classification; Deep Net; Urban Image Classification; Sentinel-2 Image; Big Data; Semantic Segmentation; Land Use and Land Cover; Loss Function Cost; Forestry; Receptive Field; deep learning; Vector Layer; Orfeo ToolBox; Deep Convolutional Neural Network; SAR Image; TensorFlow; Output Tensor; QGIS; Synthetic Aperture Radar Image; Land Cover Mapping; SAR; Python API; Optical Images; Input Image; Open Street Map Data; Panchromatic Channel; Spot-7 Image; Validation Dataset; Gps Coordinate; Python Code; Support Vector Machines