Reprint

Deep Learning and Transformers’ Methods Applied to Remotely Captured Data

Edited by
May 2024
348 pages
  • ISBN978-3-7258-0585-3 (Hardback)
  • ISBN978-3-7258-0586-0 (PDF)
https://doi.org/10.3390/books978-3-7258-0586-0 (registering)

This book is a reprint of the Topic that was published in

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Summary

The areas of machine learning and deep learning have experienced impressive progress in recent years. This progress has mainly been driven by the availability of high processing performance at an affordable cost and a large quantity of data. Most state-of-the-art techniques today are based on deep neural networks or the more recently proposed transformers. This progress has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently. Among the various research areas that have been significantly impacted by this progress is the processing of remotely captured data such as airborne and spaceborne passive and active imagery, underwater imagery, mobile mapping data, etc. This collection gathered cutting-edge contributions from researchers using deep learning and transformers for remote sensing and for processing remotely captured data.

Format
  • Hardback
License
© 2024 by the authors; CC BY-NC-ND license
Keywords
Remote Sensing; Super-Resolution; Deep Learning; Ship Detection; Satellite Imaging; Transformer Models; Image Classification; Terrain Analysis; LiDAR Data; Point Cloud Transformer