  Remote Sensing Image Change Captioning (RSICC) aims to describe surface
changes between multi-temporal remote sensing images in language, including the
changed object categories, locations, and dynamics of changing objects (e.g.,
added or disappeared). This poses challenges to spatial and temporal modeling
of bi-temporal features. Despite previous methods progressing in the spatial
change perception, there are still weaknesses in joint spatial-temporal
modeling. To address this, in this paper, we propose a novel RSCaMa model,
which achieves efficient joint spatial-temporal modeling through multiple CaMa
layers, enabling iterative refinement of bi-temporal features. To achieve
efficient spatial modeling, we introduce the recently popular Mamba (a state
space model) with a global receptive field and linear complexity into the RSICC
task and propose the Spatial Difference-aware SSM (SD-SSM), overcoming
limitations of previous CNN- and Transformer-based methods in the receptive
field and computational complexity. SD-SSM enhances the model's ability to
capture spatial changes sharply. In terms of efficient temporal modeling,
considering the potential correlation between the temporal scanning
characteristics of Mamba and the temporality of the RSICC, we propose the
Temporal-Traversing SSM (TT-SSM), which scans bi-temporal features in a
temporal cross-wise manner, enhancing the model's temporal understanding and
information interaction. Experiments validate the effectiveness of the
efficient joint spatial-temporal modeling and demonstrate the outstanding
performance of RSCaMa and the potential of the Mamba in the RSICC task.
Additionally, we systematically compare three different language decoders,
including Mamba, GPT-style decoder, and Transformer decoder, providing valuable
insights for future RSICC research. The code will be available at
\emph{\url{https://github.com/Chen-Yang-Liu/RSCaMa}}
