# A Survey on Visual Mamba
## 4 Visual Mamba in Application Fields
### Remote Sensing Image  
The progress of remote sensing methods has sparked interest in high-resolution Earth observation, with the transformer model providing an optimal solution through its attention mechanism. Its quadratic complexity, however, presents problems with memory consumption and modeling efficiency. The SSM addresses these issues by establishing long-distance dependencies with near-linear complexity, and Mamba can further enhance efficiency through hardware optimization and time-varying parameters. Representative recent work is presented in Table 9.  
By drawing inspiration from TNT, [PERSON] et al. introduced a new Mamba-in-Mamba (MiM-ISTD) [66] architecture to enhance the efficiency of infrared tiny target detection. In the proposed approach, local patches are considered \"visual sentences\", while outer Mamba is utilized to extract global information. Regarding remote sensing image classification, RSMamba [36] features a dynamic multi-path activation mechanism to improve Mamba's capability for handling non-causal data. RS-Mamba [69] is adept at handling very-high-resolution (VHR) remote sensing images for dense prediction tasks, built on an omnidirectional selective scan module to model images from various angles comprehensively. In remote sensing research, it is challenging to classify hyperspectral images because of their high-dimensional complicated data. HSIMamba [72] was designed with a module dedicated to spatial analysis, including multiple spectral bands and three-dimensional spatial structures to take advantage of the rich multidimensional nature of the hyperspectral data and to improve the feature representation capability using linear transformations and activation functions. Through the use of forward and backward spectral dependence \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Method** & **Highlight** & **Efficiency** & **Code** \\ \hline \hline Pan-sharpening & Pan-Mamba [73] & \begin{tabular}{c} channel swapping Mamba; \\ cross-modal Mamba \\ \end{tabular} & \begin{tabular}{c} Params 0.1827 \\ FLOPs 3.0088 \\ \end{tabular} & ✓ \\ \hline \hline Infrared Small & \multirow{3}{*}{MIM-ISTD [66]} & \begin{tabular}{c} Mamba-in-Mamba \\ architecture \\ \end{tabular} & \begin{tabular}{c} Params 1.16 \\ FLOPs 1.01 \\ Inference speed 30 \\ Memory 1774 \\ \end{tabular} & ✓ \\ \hline \hline Classification & \
\begin{tabular}{c} RSMamba [36] \\ \end{tabular} & multi-path activation & - & ✓ \\ \cline{2-5}  & HSIMamba [72] & process data bidirectionally & Memory 136.53 & ✓ \\ \hline Image dense prediction & RS-Mamba [69] & omnidirectional selective scan & - & ✓ \\ \hline Change detection & ChangeMamba [54] & cross-scan mechanism & - & ✓ \\ \hline \hline \multirow{3}{*}{Semantic segmentation} & RS3 Mamba [67] & dual-branch network &
\begin{tabular}{c} FLOPs 31.65 \\ Params 43.32 \\ Memory 2332 \\ \end{tabular} & ✓ \\ \cline{2-5}  & Samba [75] & encoder-decoder architecture & Params 51.9 & ✓ \\ \hline \hline \end{tabular} For the Efficiency, Inference speed is in ms, Memory is in MB, Params is in M, and FLOPS is in G.  
\end{table} 
Table 9: Representative mamba work in remote sensing image. capture, HSIMamba uses a bi-directional processing approach that enhances the network's capacity to represent and use spectrum information. In addition, Pan-Mamba [73] offers a novel network in the pansharpening space and modifies two essential elements, channel switching Mamba and cross-modal Mamba, both of which are skillfully designed for effective cross-modal information fusion and interchange. For the first time, Change-Mamba [54] investigated the Mamba architecture's potential for distant sensing change detection (CD) activities. For binary change detection (BCD), semantic change detection (SCD), and building damage assessment (BDA) tasks, the MambaBCD, MambaSCD, and MambaBDA network frameworks were built. Three spatio-temporal connection modeling mechanisms were proposed to completely learn spatio-temporal features.