  Modern change detection (CD) has achieved remarkable success by the powerful
discriminative ability of deep convolutions. However, high-resolution remote
sensing CD remains challenging due to the complexity of objects in the scene.
Objects with the same semantic concept may show distinct spectral
characteristics at different times and spatial locations. Most recent CD
pipelines using pure convolutions are still struggling to relate long-range
concepts in space-time. Non-local self-attention approaches show promising
performance via modeling dense relations among pixels, yet are computationally
inefficient. Here, we propose a bitemporal image transformer (BIT) to
efficiently and effectively model contexts within the spatial-temporal domain.
Our intuition is that the high-level concepts of the change of interest can be
represented by a few visual words, i.e., semantic tokens. To achieve this, we
express the bitemporal image into a few tokens, and use a transformer encoder
to model contexts in the compact token-based space-time. The learned
context-rich tokens are then feedback to the pixel-space for refining the
original features via a transformer decoder. We incorporate BIT in a deep
feature differencing-based CD framework. Extensive experiments on three CD
datasets demonstrate the effectiveness and efficiency of the proposed method.
Notably, our BIT-based model significantly outperforms the purely convolutional
baseline using only 3 times lower computational costs and model parameters.
Based on a naive backbone (ResNet18) without sophisticated structures (e.g.,
FPN, UNet), our model surpasses several state-of-the-art CD methods, including
better than four recent attention-based methods in terms of efficiency and
accuracy. Our code is available at https://github.com/justchenhao/BIT\_CD.
