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Author: 斯坦泽尔

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Deep learning for remote sensing images with open source software /

"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 s...

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Bibliographic Details
Main Authors: Cresson, Re?mi
Published: CRC Press,
Publisher Address: Boca Raton, FL :
Publication Dates: [2020]
Literature type: Book
Language: English
Series: Signal and image processing of Earth observations series
Subjects:
Remote sensing > Data processing.
Remote-sensing images.
Image processing > Digital techniques.
Machine learning.
Neural networks (Computer science)
Open source software.
Summary: "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"--
Carrier Form: xi 151 pages : color illustrations, color maps ; 25 cm.
Bibliography: Includes bibliographical references (pages 145-148) and index.
ISBN: 9780367518981
0367518988
9780367858483
0367858487
Index Number: G70
CLC: TP75
Call Number: TP75/C922
Contents: Deep learning background -- 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.

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