Transformers in Remote Sensing: A Survey

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.

Automated summary

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.