Cresson, Rémi,
Deep learning for remote sensing images with open source software / Rémi Cresson. - First edition. - 1 online resource (xi, 151 pages) : color illustrations. - Signal and image processing of Earth observation series .
Deep learning backgrounds -- Software -- Data used : the Tokyo dataset -- A simple convolutional neural network -- Fully convolutional neural network -- Classifiers on deep features -- Dealing with multiple sources -- Semantic segmentation of optical imagery -- Data used : the Amsterdam dataset -- Mapping buildings -- Gap filling of optical images : principle -- The Marmande dataset -- Pre-processing -- Model training -- Inference.
"In today's world, deep learning source codes and a plethora of open access geospatial images are available, but readers are missing the educational tools. 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 many applications for various remote sensing images processing in landcover mapping, forestry, urban, in disaster mapping, image restoration, etc. Written with practitioners and students in mind, this book helps readers link together the theory and practical use of existing tools and data to create their own remote sensing data processing"--
9781000093599 100009359X 9781003020851 1003020852 9781000093612 1000093611 9781000093605 1000093603
10.1201/9781003020851 doi
Remote sensing--Data processing.
Remote-sensing images.
Image processing--Digital techniques.
Machine learning.
Neural networks (Computer science)
Open source software.
TECHNOLOGY / Remote Sensing
TECHNOLOGY / Imaging Systems
COMPUTERS / Computer Graphics / Image Processing (see also PHOTOGRAPHY / Techniques / Digital)
G70.4 / .C75 2020
621.36/78
Deep learning for remote sensing images with open source software / Rémi Cresson. - First edition. - 1 online resource (xi, 151 pages) : color illustrations. - Signal and image processing of Earth observation series .
Deep learning backgrounds -- Software -- Data used : the Tokyo dataset -- A simple convolutional neural network -- Fully convolutional neural network -- Classifiers on deep features -- Dealing with multiple sources -- Semantic segmentation of optical imagery -- Data used : the Amsterdam dataset -- Mapping buildings -- Gap filling of optical images : principle -- The Marmande dataset -- Pre-processing -- Model training -- Inference.
"In today's world, deep learning source codes and a plethora of open access geospatial images are available, but readers are missing the educational tools. 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 many applications for various remote sensing images processing in landcover mapping, forestry, urban, in disaster mapping, image restoration, etc. Written with practitioners and students in mind, this book helps readers link together the theory and practical use of existing tools and data to create their own remote sensing data processing"--
9781000093599 100009359X 9781003020851 1003020852 9781000093612 1000093611 9781000093605 1000093603
10.1201/9781003020851 doi
Remote sensing--Data processing.
Remote-sensing images.
Image processing--Digital techniques.
Machine learning.
Neural networks (Computer science)
Open source software.
TECHNOLOGY / Remote Sensing
TECHNOLOGY / Imaging Systems
COMPUTERS / Computer Graphics / Image Processing (see also PHOTOGRAPHY / Techniques / Digital)
G70.4 / .C75 2020
621.36/78