Transformers in Remote Sensing: A Survey
By Abdulaziz Amer Aleissaee, Amandeep Kumar, Rao Muhammad Anwer, Salman Hameed Khan, Hisham Cholakkal, Guisong Xia, Fahad Shahbaz Khan. Remote Sensing, 2022
379 citations recorded by the source corpus.
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.
Subfields: Media Technology
View Paper: https://www.semanticscholar.org/paper/744ee04b08e04ad216bd586ac74bbc1fed0dea8f
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In Media Technology
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Citations in the corpus
- Multimodal Fusion Transformer for Remote Sensing Image Classification (2022, positive): “Inspired by the success of transformers in computer vision [49], several new transformer models have been developed in the last few years [50].”
- The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot (2023, background): “Despite advancements in segmentation techniques, including the advances of artificial intelligence (AI) with deep learning-based methods [4, 2], a key challenge remains: effective segmentation of images with minimal human input.”