Visual State Space backbone fused with explicit spatio–frequency cues, bidirectional change guidance, and class-imbalance-aware loss—delivering robust, precise semantic change detection under tough illumination/seasonal shifts and severe long-tail labels.
- Mar 2026 - Notebook Released For an interactive workflow, use the notebook ✨✨
annotations/MambaFCS.ipynb✨✨. - Mar 2026 - Weights + Notebook Released — Official Mamba-FCS checkpoints are now available on 🤗🤗Hugging Face🤗🤗.
- Feb 2026 - Paper Published — IEEE JSTARS (Official DOI: https://doi.org/10.1109/JSTARS.2026.3663066)
- Jan 2026 - Accepted — IEEE JSTARS (Camera-ready version submitted)
- Jan 2026 - Code Released — Full training pipeline with structured YAML configurations is now available
- Aug 2025 - Preprint Released — Preprint available on arXiv: https://arxiv.org/abs/2508.08232
Ready to push the boundaries of change detection? Let's go.
Semantic Change Detection in remote sensing is tough: seasonal shifts, lighting variations, and severe class imbalance constantly trip up traditional methods.
Mamba-FCS changes the game:
- VMamba backbone → linear-time long-range modeling (no more transformer VRAM nightmares)
- Joint spatio–frequency fusion → injects FFT log-amplitude cues into spatial features for appearance invariance + sharper boundaries
- CGA module → change probabilities actively guide semantic refinement (and vice versa)
- SeK Loss → finally treats rare classes with the respect they deserve
Outcome: cleaner maps, stronger rare-class recall, and real-world resilience.
Spatial power + frequency smarts + change-guided attention = next-level SCD
The frequency domain is known to reveal latent structures in signals that remain obscure in the spatial domain.
Building on this premise, we explore whether Fourier transformation of latent representations can expose similarly discriminative hidden features.
This spatio–frequency, change-guided design is a key factor underlying improved rare-class performance and sharper semantic boundary delineation.
Feed in bi-temporal images T1 and T2:
- VMamba encoder extracts rich multi-scale features from both timestamps
- JSF injects frequency-domain log-amplitude (FFT) into spatial features → stronger invariance to illumination/seasonal shifts
- CGA leverages change cues to tighten BCD ↔ SCD synergy
- Lightweight decoder predicts the final semantic change map
- SeK Loss drives balanced optimization, even when changed pixels are scarce
Simple. Smart. Superior.
Pretrained Mamba-FCS checkpoints are now hosted on Hugging Face: buddhi19/MambaFCS.
Use these weights directly for inference and evaluation, or keep them alongside your experiment checkpoints for quick benchmarking.
| Model | Links |
|---|---|
| VMamba-Tiny | Zenodo • GDrive • BaiduYun |
| VMamba-Small | Zenodo • GDrive • BaiduYun |
| VMamba-Base | Zenodo • GDrive • BaiduYun |
Set pretrained_weight_path in your YAML to the downloaded .pth.
git clone https://github.com/Buddhi19/MambaFCS.git
cd MambaFCS
conda create -n mambafcs python=3.10 -y
conda activate mambafcs
pip install --upgrade pip
pip install -r requirements.txt
pip install pyyamlcd kernels/selective_scan
pip install .
cd ../../..(Pro tip: match your torch CUDA version with nvcc/GCC if you hit issues.)
Plug-and-play support for SECOND and Landsat-SCD.
/path/to/SECOND/
├── train/
│ ├── A/ # T1 images
│ ├── B/ # T2 images
│ ├── labelA/ # T1 class IDs (single-channel)
│ └── labelB/ # T2 class IDs
├── test/
│ ├── A/
│ ├── B/
│ ├── labelA/
│ └── labelB/
├── train.txt
└── test.txt
Same idea, with train_list.txt, val_list.txt, test_list.txt.
Must-do: Use integer class maps (not RGB). Convert palettes first.
YAML-driven — clean and flexible.
-
Edit paths in
configs/train_LANDSAT.yamlorconfigs/train_SECOND.yaml -
Fire it up:
# Landsat-SCD
python train.py --config configs/train_LANDSAT.yaml
# SECOND
python train.py --config configs/train_SECOND.yamlCheckpoints + TensorBoard logs land in saved_models/<your_name>/.
Resume runs? Just flip resume: true and point to optimizer/scheduler states.
For an interactive workflow, use the notebook annotations/MambaFCS.ipynb.
It is set up for users who want to:
- run evaluations interactively
- inspect predictions and qualitative outputs
- perform annotation and review in a notebook-driven workflow
Pair it with the released checkpoints on Hugging Face for fast experimentation without retraining.
Straight from the paper — reproducible out of the box:
| Method | Dataset | OA (%) | FSCD (%) | mIoU (%) | SeK (%) |
|---|---|---|---|---|---|
| SCanNet | SECOND | 87.86 | 63.66 | 73.42 | 23.94 |
| ChangeMamba | SECOND | 88.12 | 64.03 | 73.68 | 24.11 |
| Mamba-FCS | SECOND | 88.62 | 65.78 | 74.07 | 25.50 |
| SCanNet | Landsat-SCD | 96.04 | 85.62 | 86.37 | 52.63 |
| ChangeMamba | Landsat-SCD | 96.08 | 86.61 | 86.91 | 53.66 |
| Mamba-FCS | Landsat-SCD | 96.25 | 89.27 | 88.81 | 60.26 |
This work is strongly influenced by prior advances in state-space vision backbones and Mamba-based change detection. In particular, we acknowledge:
- VMamba (Visual State Space Models for Vision) — backbone inspiration: https://github.com/MzeroMiko/VMamba
- ChangeMamba — Mamba-style change detection inspiration: https://github.com/ChenHongruixuan/ChangeMamba.git
If Mamba-FCS fuels your research, please cite:
@ARTICLE{mambafcs,
author={Wijenayake, Buddhi and Ratnayake, Athulya and Sumanasekara, Praveen and Godaliyadda, Roshan and Ekanayake, Parakrama and Herath, Vijitha and Wasalathilaka, Nichula},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
title={Mamba-FCS: Joint Spatio-Frequency Feature Fusion, Change-Guided Attention, and SeK Inspired Loss for Enhanced Semantic Change Detection in Remote Sensing},
year={2026},
volume={19},
number={},
pages={7680-7698},
keywords={Semantics;Feature extraction;Transformers;Remote sensing;Frequency-domain analysis;Decoding;Computational modeling;Computer architecture;Context modeling;Lighting;Remote sensing imagery;semantic change detection (CD);separated Kappa (SeK);spatial–frequency fusion;state-space models (SSMs)},
doi={10.1109/JSTARS.2026.3663066}}
You might consider citing:
@INPROCEEDINGS{11450773,
author={Wijenayake, W.M.B.S.K. and Ratnayake, R.M.A.M.B. and Sumanasekara, D.M.U.P. and Wasalathilaka, N.S. and Piratheepan, M. and Godaliyadda, G.M.R.I. and Ekanayake, M.P.B. and Herath, H.M.V.R.},
booktitle={2025 IEEE 19th International Conference on Industrial and Information Systems (ICIIS)},
title={Precision Spatio-Temporal Feature Fusion for Robust Remote Sensing Change Detection},
year={2026},
volume={19},
number={},
pages={557-562},
keywords={Accuracy;Computational modeling;Pipelines;Feature extraction;Transformers;Decoding;Remote sensing;Optimization;Monitoring;Context modeling;Remote Sensing;Binary Change Detection;State Space Models;Mamba},
doi={10.1109/ICIIS69028.2026.11450773}}
@INPROCEEDINGS{11217111,
author={Ratnayake, R.M.A.M.B. and Wijenayake, W.M.B.S.K. and Sumanasekara, D.M.U.P. and Godaliyadda, G.M.R.I. and Herath, H.M.V.R. and Ekanayake, M.P.B.},
booktitle={2025 Moratuwa Engineering Research Conference (MERCon)},
title={Enhanced SCanNet with CBAM and Dice Loss for Semantic Change Detection},
year={2025},
volume={},
number={},
pages={84-89},
keywords={Training;Accuracy;Attention mechanisms;Sensitivity;Semantics;Refining;Feature extraction;Transformers;Power capacitors;Remote sensing},
doi={10.1109/MERCon67903.2025.11217111}}- Shen, H., et al. (2026)
Foundation Model-Driven Semantic Change Detection in Remote Sensing Imagery
📄 arXiv:2602.13780