UNetMamba: An Efficient UNet-Like Mamba for Semantic Segmentation of High-Resolution Remote Sensing Images
<|reference_start|>UNetMamba: An Efficient UNet-Like Mamba for Semantic Segmentation of High-Resolution Remote Sensing Images: Semantic segmentation of high-resolution remote sensing images is vital in downstream applications such as land-cover mapping, urban planning and disaster assessment.Existing Transformer-based methods suffer from the constraint between accuracy and efficiency, while the recently proposed Mamba is renowned for being efficient. Therefore, to overcome the dilemma, we propose UNetMamba, a UNet-like semantic segmentation model based on Mamba. It incorporates a mamba segmentation decoder (MSD) that can efficiently decode the complex information within high-resolution images, and a local supervision module (LSM), which is train-only but can significantly enhance the perception of local contents. Extensive experiments demonstrate that UNetMamba outperforms the state-of-the-art methods with mIoU increased by 0.87% on LoveDA and 0.36% on ISPRS Vaihingen, while achieving high efficiency through the lightweight design, less memory footprint and reduced computational cost. The source code is available at https://github.com/EnzeZhu2001/UNetMamba.<|reference_end|>
@article{zhu2024unetmamba:,
    title={UNetMamba: An Efficient UNet-Like Mamba for Semantic Segmentation of
  High-Resolution Remote Sensing Images},
    author={Enze Zhu, Zhan Chen, Dingkai Wang, Hanru Shi, Xiaoxuan Liu and Lei
  Wang},
    journal={arXiv preprint arXiv:2408.11545},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11545},
    primaryClass={cs.CV}
}