UNetMamba：一种用于高分辨率遥感图像语义分割的高效类UNet Mamba模型
UNetMamba: An Efficient UNet-Like Mamba for Semantic Segmentation of High-Resolution Remote Sensing Images
AI总结：
针对高分辨率遥感图像语义分割中Transformer方法精度与效率的制约，提出类UNet的Mamba模型UNetMamba，结合Mamba分割解码器和局部监督模块，在LoveDA和ISPRS Vaihingen数据集上mIoU提升且效率更高。
摘要：
高分辨率遥感图像的语义分割在土地覆盖制图、城市规划和灾害评估等下游应用中至关重要。现有基于Transformer的方法存在精度与效率之间的制约，而最近提出的Mamba以高效著称。因此，为克服这一困境，我们提出UNetMamba，一种基于Mamba的类UNet语义分割模型。它包含一个能高效解码高分辨率图像中复杂信息的Mamba分割解码器（MSD），以及一个仅用于训练但可显著增强局部内容感知的局部监督模块（LSM）。大量实验表明，UNetMamba在LoveDA数据集上mIoU提升0.87%，在ISPRS Vaihingen数据集上提升0.39%，同时通过轻量化设计实现高效，内存占用更少、计算成本更低。源代码可在https://github.com/EnzeZhu2001/UNetMamba获取。
英文摘要：
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 0.39% 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
