UNetMamba: An Efficient UNet-Like Mamba for Semantic Segmentation of High-Resolution Remote Sensing Images

TL;DR

UNetMamba is a lightweight, efficient UNet-like model that improves semantic segmentation accuracy of high-resolution remote sensing images by incorporating a novel decoder and local supervision, outperforming state-of-the-art methods.

Contribution

The paper introduces UNetMamba, a novel UNet-like model with a mamba segmentation decoder and local supervision module, enhancing efficiency and accuracy in high-resolution remote sensing image segmentation.

Findings

Outperforms state-of-the-art methods in mIoU on LoveDA and ISPRS Vaihingen datasets.

Achieves high efficiency with lightweight design, less memory, and lower computational cost.

Demonstrates significant accuracy improvements over existing Transformer-based methods.

Abstract

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.