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Deep Learning and Transformers’ Methods Applied to Remotely Captured Data
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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.
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Keywords
- deep learning
- image classification
- LiDAR data
- Point Cloud Transformer
- Remote sensing
- Satellite Imaging
- ship detection
- super-resolution
- terrain analysis
- transformer models
Links
DOI: 10.3390/books978-3-7258-0586-0Editions