  Remote sensing image classification forms the foundation of various
understanding tasks, serving a crucial function in remote sensing image
interpretation. The recent advancements of Convolutional Neural Networks (CNNs)
and Transformers have markedly enhanced classification accuracy. Nonetheless,
remote sensing scene classification remains a significant challenge, especially
given the complexity and diversity of remote sensing scenarios and the
variability of spatiotemporal resolutions. The capacity for whole-image
understanding can provide more precise semantic cues for scene discrimination.
In this paper, we introduce RSMamba, a novel architecture for remote sensing
image classification. RSMamba is based on the State Space Model (SSM) and
incorporates an efficient, hardware-aware design known as the Mamba. It
integrates the advantages of both a global receptive field and linear modeling
complexity. To overcome the limitation of the vanilla Mamba, which can only
model causal sequences and is not adaptable to two-dimensional image data, we
propose a dynamic multi-path activation mechanism to augment Mamba's capacity
to model non-causal data. Notably, RSMamba maintains the inherent modeling
mechanism of the vanilla Mamba, yet exhibits superior performance across
multiple remote sensing image classification datasets. This indicates that
RSMamba holds significant potential to function as the backbone of future
visual foundation models. The code will be available at
\url{https://github.com/KyanChen/RSMamba}.
