Contextual Learning in Fourier Complex Field for VHR Remote Sensing Images

TL;DR

This paper introduces a Fourier-based complex self-attention mechanism for transformer models, enabling efficient and effective classification of very high-resolution remote sensing images by reducing computational complexity.

Contribution

It proposes a novel Fourier complex transformer with a complex self-attention mechanism that models high-order contextual information efficiently for VHR RS image classification.

Findings

FCT outperforms existing models on VHR RS datasets.

CSA reduces computational cost by over 50%.

The model stabilizes training with the Logmax normalization.

Abstract

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.