The DOI matches its Crossref record; 127 of the 127 references it cites were found in a registry or an index.
Transformers in Remote Sensing: A Survey
Open accessAbstract
Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformer-based architectures, originally introduced in natural language processing, have pervaded computer vision field where the self-attention mechanism has been utilized as a replacement to the popular convolution operator for capturing long-range dependencies. Inspired by recent advances in computer vision, the remote sensing community has also witnessed an increased exploration of vision transformers for a diverse set of tasks. Although a number of surveys have focused on transformers in computer vision in general, to the best of our knowledge we are the first to present a systematic review of recent advances based on transformers in remote sensing. Our survey covers more than 60 recent transformer-based methods for different remote sensing problems in sub-areas of remote sensing: very high-resolution (VHR), hyperspectral (HSI) and synthetic aperture radar (SAR) imagery. We conclude the survey by discussing different challenges and open issues of transformers in remote sensing.
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1 retracted
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Cited by
Foundation Models for Generalist Geospatial Artificial Intelligence
Jakubik, J., Roy, S., Phillips, C. E., Fraccaro, P., Godwin, D., Zadrozny, B., Szwarcman, D., Gomes, C., Nyirjesy, G., Edwards, B., et al. (2023) · arXiv · Cited by 157
Metadata
- Authors
- Aleissaee, A. A., Kumar, A., Anwer, R. M., Khan, S., Cholakkal, H., Xia, G.-S., & Khan, F. S.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2023-03-30
- Volume
- 15
- Issue
- 7
- Pages
- 1860
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W4362519158
- Cited by
- 417
- References
- 127
- FWCI
- 45.94
- Type
- journal article
- Language
- en
- Open access
- gold
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- Aleissaee, A. A., Kumar, A., Anwer, R. M., Khan, S., Cholakkal, H., Xia, G.-S., & Khan, F. S. (2023). Transformers in Remote Sensing: A Survey. Remote Sensing. https://doi.org/10.3390/rs15071860
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