  Very high-resolution (VHR) remote sensing (RS) image classification is the
fundamental task for RS image analysis and understanding. Recently,
transformer-based models demonstrated outstanding potential for learning
high-order contextual relationships from natural images with general resolution
(224x224 pixels) and achieved remarkable results on general image
classification tasks. However, the complexity of the naive transformer grows
quadratically with the increase in image size, which prevents transformer-based
models from VHR RS image (500x500 pixels) classification and other
computationally expensive downstream tasks. To this end, we propose to
decompose the expensive self-attention (SA) into real and imaginary parts via
discrete Fourier transform (DFT) and therefore propose an efficient complex
self-attention (CSA) mechanism. Benefiting from the conjugated symmetric
property of DFT, CSA is capable to model the high-order contextual information
with less than half computations of naive SA. To overcome the gradient
explosion in Fourier complex field, we replace the Softmax function with the
carefully designed Logmax function to normalize the attention map of CSA and
stabilize the gradient propagation. By stacking various layers of CSA blocks,
we propose the Fourier Complex Transformer (FCT) model to learn global
contextual information from VHR aerial images following the hierarchical
manners. Universal experiments conducted on commonly used RS classification
data sets demonstrate the effectiveness and efficiency of FCT, especially on
very high-resolution RS images.
