  Remote sensing image change captioning (RSICC) aims to automatically generate
sentences that describe content differences in remote sensing bitemporal
images. Recently, attention-based transformers have become a prevalent idea for
capturing the features of global change. However, existing transformer-based
RSICC methods face challenges, e.g., high parameters and high computational
complexity caused by the self-attention operation in the transformer encoder
component. To alleviate these issues, this paper proposes a Sparse Focus
Transformer (SFT) for the RSICC task. Specifically, the SFT network consists of
three main components, i.e. a high-level features extractor based on a
convolutional neural network (CNN), a sparse focus attention mechanism-based
transformer encoder network designed to locate and capture changing regions in
dual-temporal images, and a description decoder that embeds images and words to
generate sentences for captioning differences. The proposed SFT network can
reduce the parameter number and computational complexity by incorporating a
sparse attention mechanism within the transformer encoder network. Experimental
results on various datasets demonstrate that even with a reduction of over 90\%
in parameters and computational complexity for the transformer encoder, our
proposed network can still obtain competitive performance compared to other
state-of-the-art RSICC methods. The code can be available at
