Journal
REMOTE SENSING
Volume 15, Issue 7, Pages -Publisher
MDPI
DOI: 10.3390/rs15071860
Keywords
remote sensing; transformers; survey
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Deep learning algorithms have gained popularity in remote sensing image analysis, and transformer-based architectures have been widely used in computer vision with self-attention mechanism replacing convolution operator. Inspired by this, the remote sensing community has explored vision transformers for various tasks. This survey presents a systematic review of recent transformer-based methods in remote sensing, covering different sub-areas like very high-resolution (VHR), hyperspectral (HSI), and synthetic aperture radar (SAR) imagery. The survey concludes by discussing challenges and open issues of transformers in remote sensing.
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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