  Deep learning-based algorithms have seen a massive popularity in different
areas of remote sensing image analysis over the past decade. Recently,
transformers-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, 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 transformers-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. Additionally, we intend to frequently update and maintain the latest
transformers in remote sensing papers with their respective code at:
https://github.com/VIROBO-15/Transformer-in-Remote-Sensing

Computer Vision and Pattern Recognition