# FusionMamba: Efficient Remote Sensing Image Fusion with State Space Model arXiv: https://arxiv.org/abs/2404.07932 ยท HTML: https://mlsanity.com/paper/2404.07932/ ## Key facts - Cited by 21 papers in the map (data through 14 Oct 2026). - Placed in Point clouds, detection and remote sensing, one of 10 directions in State-space sequence models (S4, Mamba). - Builds on 9 of its 49 references that are in the map. - First posted on arXiv 11 Apr 2024. ## Placed in - State-space sequence models (S4, Mamba) / [Point clouds, detection and remote sensing](https://mlsanity.com/ssm/point-clouds-detection-and-remote-sensing/) ## Builds on (most cited first, 9 of 9) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023) - [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021) - [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024) - [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024) - [Simplified State Space Layers for Sequence Modeling](https://mlsanity.com/paper/2208.04933/) (2022) - [Hungry Hungry Hippos: Towards Language Modeling with State Space Models](https://mlsanity.com/paper/2212.14052/) (2022) - [Pan-Mamba: Effective pan-sharpening with State Space Model](https://mlsanity.com/paper/2402.12192/) (2024) - [MiM-ISTD: Mamba-in-Mamba for Efficient Infrared Small Target Detection](https://mlsanity.com/paper/2403.02148/) (2024) ## Built on by (most cited first, 21 of 21) - [A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond](https://mlsanity.com/paper/2410.02362/) (2024) - [MaeFuse: Transferring Omni Features with Pretrained Masked Autoencoders for Infrared and Visible Image Fusion via Guided Training](https://mlsanity.com/paper/2404.11016/) (2024) - [Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation](https://mlsanity.com/paper/2508.04022/) (2025) - [Shuffle Mamba: State Space Models with Random Shuffle for Multi-Modal Image Fusion](https://mlsanity.com/paper/2409.01728/) (2024) - [Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook](https://mlsanity.com/paper/2505.00630/) (2025) - [Task-Generalized Adaptive Cross-Domain Learning for Multimodal Image Fusion](https://mlsanity.com/paper/2508.15505/) (2025) - [SLGNet: Synergizing Structural Priors and Language-Guided Modulation for Multimodal Object Detection](https://mlsanity.com/paper/2601.02249/) (2026) - [PAN-Crafter: Learning Modality-Consistent Alignment for PAN-Sharpening](https://mlsanity.com/paper/2505.23367/) (2025) - [SaFiRe: Saccade-Fixation Reiteration with Mamba for Referring Image Segmentation](https://mlsanity.com/paper/2510.10160/) (2025) - [Cross-modal State Space Modeling for Real-time RGB-thermal Wild Scene Semantic Segmentation](https://mlsanity.com/paper/2506.17869/) (2025) - [UAVD-Mamba: Deformable Token Fusion Vision Mamba for Multimodal UAV Detection](https://mlsanity.com/paper/2507.00849/) (2025) - [TUNI: Unifying Pre-training and Fine-tuning with Modality-Aware Mutual Learning and Rectification for RGB-T Semantic Segmentation](https://mlsanity.com/paper/2509.10005/) (2025) - [Enpowering Your Pansharpening Models with Generalizability: Unified Distribution is All You Need](https://mlsanity.com/paper/2510.22217/) (2025) - [Cross-Scale Pansharpening via ScaleFormer and the PanScale Benchmark](https://mlsanity.com/paper/2603.00543/) (2026) - [G-ZAP: A Generalizable Zero-Shot Framework for Arbitrary-Scale Pansharpening](https://mlsanity.com/paper/2603.14412/) (2026) - [ASSR-Net: Anisotropic Structure-Aware and Spectrally Recalibrated Network for Hyperspectral Image Fusion](https://mlsanity.com/paper/2604.05742/) (2026) - [MambaX: Image Super-Resolution with State Predictive Control](https://mlsanity.com/paper/2511.18028/) (2025) - [S2WMamba: A Wavelet-Assisted Mamba-Based Dual-Branch Network For Pansharpening](https://mlsanity.com/paper/2512.06330/) (2025) - [Reversible Efficient Diffusion for Image Fusion](https://mlsanity.com/paper/2601.20260/) (2026) - [AMIF: Authorizable Medical Image Fusion Model with Built-in Authentication](https://mlsanity.com/paper/2603.24296/) (2026) - [SCoPE-Reg: Efficient Rigid Ultrasound Slice-to-Volume Registration via State-Space Correlation and Closed-Form Pose Estimation](https://mlsanity.com/paper/2608.28715/) (2026) ## Abstract (arXiv) Remote sensing image fusion aims to generate a high-resolution multi/hyper-spectral image by combining a high-resolution image with limited spectral data and a low-resolution image rich in spectral information. Current deep learning (DL) methods typically employ convolutional neural networks (CNNs) or Transformers for feature extraction and information integration. While CNNs are efficient, their limited receptive fields restrict their ability to capture global context. Transformers excel at learning global information but are computationally expensive. Recent advancements in the state space model (SSM), particularly Mamba, present a promising alternative by enabling global perception with low complexity. However, the potential of SSM for information integration remains largely unexplored. Therefore, we propose FusionMamba, an innovative method for efficient remote sensing image fusion. Our contributions are twofold. First, to effectively merge spatial and spectral features, we expand the single-input Mamba block to accommodate dual inputs, creating the FusionMamba block, which serves as a plug-and-play solution for information integration. Second, we incorporate Mamba and FusionMamba blocks into an interpretable network architecture tailored for remote sensing image fusion. Our designs utilize two U-shaped network branches, each primarily composed of four-directional Mamba blocks, to extract spatial and spectral features separately and hierarchically. The resulting feature maps are sufficiently merged in an auxiliary network branch constructed with FusionMamba blocks. Furthermore, we improve the representation of spectral information through an enhanced channel attention module. Quantitative and qualitative valuation results across six datasets demonstrate that our method achieves SOTA performance. The code is available at https://github.com/PSRben/FusionMamba.