# RSCaMa: Remote Sensing Image Change Captioning with State Space Model

arXiv: https://arxiv.org/abs/2404.18895 · HTML: https://mlsanity.com/paper/2404.18895/

## Key facts

- Cited by 35 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 13 of its 22 references that are in the map.
- First posted on arXiv 29 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, 13 of 13)

- [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)
- [RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model](https://mlsanity.com/paper/2306.16269/) (2023)
- [RSMamba: Remote Sensing Image Classification with State Space Model](https://mlsanity.com/paper/2403.19654/) (2024)
- [RS-Mamba for Large Remote Sensing Image Dense Prediction](https://mlsanity.com/paper/2404.02668/) (2024)
- [Pan-Mamba: Effective pan-sharpening with State Space Model](https://mlsanity.com/paper/2402.12192/) (2024)
- [State Space Model for New-Generation Network Alternative to Transformers: A Survey](https://mlsanity.com/paper/2404.09516/) (2024)
- [Describing and Localizing Multiple Changes with Transformers](https://mlsanity.com/paper/2103.14146/) (2021)
- [Changes to Captions: An Attentive Network for Remote Sensing Change Captioning](https://mlsanity.com/paper/2304.01091/) (2023)
- [Samba: Semantic Segmentation of Remotely Sensed Images with State Space Model](https://mlsanity.com/paper/2404.01705/) (2024)
- [Progressive Scale-aware Network for Remote sensing Image Change Captioning](https://mlsanity.com/paper/2303.00355/) (2023)
- [T-Mamba: A unified framework with Long-Range Dependency in dual-domain for 2D & 3D Tooth Segmentation](https://mlsanity.com/paper/2404.01065/) (2024)

## Built on by (most cited first, 30 of 35)

- [Vision Mamba: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2405.04404/) (2024)
- [CDMamba: Incorporating Local Clues into Mamba for Remote Sensing Image Binary Change Detection](https://mlsanity.com/paper/2406.04207/) (2024)
- [MetaEarth: A Generative Foundation Model for Global-Scale Remote Sensing Image Generation](https://mlsanity.com/paper/2405.13570/) (2024)
- [DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensing](https://mlsanity.com/paper/2407.08132/) (2024)
- [Semantic-CC: Boosting Remote Sensing Image Change Captioning via Foundational Knowledge and Semantic Guidance](https://mlsanity.com/paper/2407.14032/) (2024)
- [RSRefSeg 2: Decoupling Referring Remote Sensing Image Segmentation with Foundation Models](https://mlsanity.com/paper/2507.06231/) (2025)
- [Enhancing Perception of Key Changes in Remote Sensing Image Change Captioning](https://mlsanity.com/paper/2409.12612/) (2024)
- [AVS-Mamba: Exploring Temporal and Multi-modal Mamba for Audio-Visual Segmentation](https://mlsanity.com/paper/2501.07810/) (2025)
- [Heterogeneous Mixture of Experts for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2502.09654/) (2025)
- [RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing](https://mlsanity.com/paper/2503.10392/) (2025)
- [Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentation](https://mlsanity.com/paper/2508.04022/) (2025)
- [Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook](https://mlsanity.com/paper/2505.00630/) (2025)
- [DVP-MVS++: Synergize Depth-Normal-Edge and Harmonized Visibility Prior for Multi-View Stereo](https://mlsanity.com/paper/2506.13215/) (2025)
- [MGIMM: Multi-Granularity Instruction Multimodal Model for Attribute-Guided Remote Sensing Image Detailed Description](https://mlsanity.com/paper/2406.04716/) (2024)
- [MV-CC: Mask Enhanced Video Model for Remote Sensing Change Caption](https://mlsanity.com/paper/2410.23946/) (2024)
- [SeG-SR: Integrating Semantic Knowledge into Remote Sensing Image Super-Resolution via Vision-Language Model](https://mlsanity.com/paper/2505.23010/) (2025)
- [SAM Guided Semantic and Motion Changed Region Mining for Remote Sensing Change Captioning](https://mlsanity.com/paper/2511.21420/) (2025)
- [Efficient Semantic Splatting for Remote Sensing Multi-view Segmentation](https://mlsanity.com/paper/2412.05969/) (2024)
- [Wavelet-Assisted Mamba for Satellite-Derived Sea Surface Temperature Super-Resolution](https://mlsanity.com/paper/2509.24334/) (2025)
- [DFM: Difference Feature Modeling with Text-Guided Gated Contrastive Loss for Remote Sensing Image Change Captioning](https://mlsanity.com/paper/2606.27410/) (2026)
- [TriDF: Triplane-Accelerated Density Fields for Few-Shot Remote Sensing Novel View Synthesis](https://mlsanity.com/paper/2503.13347/) (2025)
- [UAV as Urban Construction Change Monitor: A New Benchmark and Change Captioning Model](https://mlsanity.com/paper/2605.04409/) (2026)
- [MetaEarth-MM: Unified Multimodal Remote Sensing Image Generation with Scene-centered Joint Modeling](https://mlsanity.com/paper/2605.20090/) (2026)
- [A Benchmark for Multi-Lingual Vision-Language Learning in Remote Sensing Image Captioning](https://mlsanity.com/paper/2503.04592/) (2025)
- [Tri-path DINO: Feature Complementary Learning for Remote Sensing Multi-Class Change Detection](https://mlsanity.com/paper/2603.01498/) (2026)
- [CloudMamba: An Uncertainty-Guided Dual-Scale Mamba Network for Cloud Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2604.06844/) (2026)
- [Masks Can Talk: Extracting Structured Text Information from Single-Modal Images for Remote Sensing Change Detection](https://mlsanity.com/paper/2605.07178/) (2026)
- [HiSem: Hierarchical Semantic Disentangling for Remote Sensing Image Change Captioning](https://mlsanity.com/paper/2605.15024/) (2026)
- [Efficient Remote Sensing Instance Segmentation with Linear-Time State Space Distilled Visual Foundation Models](https://mlsanity.com/paper/2606.25324/) (2026)
- [State Space Models Meet Remote Sensing: A Survey](https://mlsanity.com/paper/2606.25329/) (2026)

## Abstract (arXiv)

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 emphurlhttps://github.com/Chen-Yang-Liu/RSCaMa