Deep learning for remote sensing images with open source software / R©♭mi Cresson.
| Author/creator | Cresson, R©♭mi. |
| Format | Electronic |
| Publication Info | Boca Raton, FL : CRC Press, Taylor & Francis Group, [2020] |
| Description | 1 online resource |
| Supplemental Content | Full text available from Taylor & Francis eBooks |
| Subjects |
| Series | Signal and Image Processing of Earth Observations Series |
| Contents | 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. |
| Abstract | "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"-- Provided by publisher. |
| Bibliography note | Includes bibliographical references and index. |
| Access restriction | Available only to authorized users. |
| Technical details | Mode of access: World Wide Web |
| Source of description | Description based on print version record and CIP data provided by publisher. |
| Issued in other form | Print version: Cresson, R©♭mi. Deep learning for remote sensing images with open source software Boca Raton, FL : CRC Press, Taylor & Francis Group, [2020] 9780367858483 |
| Genre/form | Electronic books. |
| LCCN | 2020015983 |
| ISBN | 9781003020851 (ebook) |
| ISBN | (hardback) |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | Access Content Online | ✔ Available |