CM-UNet: Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentation

TL;DR

CM-UNet is a hybrid CNN-Mamba architecture designed for remote sensing image segmentation, effectively capturing long-range dependencies and multi-scale features, outperforming existing methods on benchmark datasets.

Contribution

Introduces a novel CM-UNet model combining CNN and Mamba blocks with attention mechanisms for improved remote sensing image segmentation.

Findings

Outperforms existing methods on three benchmark datasets.

Effectively captures long-range dependencies and multi-scale features.

Demonstrates superior performance metrics across various benchmarks.

Abstract

Due to the large-scale image size and object variations, current CNN-based and Transformer-based approaches for remote sensing image semantic segmentation are suboptimal for capturing the long-range dependency or limited to the complex computational complexity. In this paper, we propose CM-UNet, comprising a CNN-based encoder for extracting local image features and a Mamba-based decoder for aggregating and integrating global information, facilitating efficient semantic segmentation of remote sensing images. Specifically, a CSMamba block is introduced to build the core segmentation decoder, which employs channel and spatial attention as the gate activation condition of the vanilla Mamba to enhance the feature interaction and global-local information fusion. Moreover, to further refine the output features from the CNN encoder, a Multi-Scale Attention Aggregation (MSAA) module is employed to merge the different scale features. By integrating the CSMamba block and MSAA module, CM-UNet effectively captures the long-range dependencies and multi-scale global contextual information of large-scale remote-sensing images. Experimental results obtained on three benchmarks indicate that the proposed CM-UNet outperforms existing methods in various performance metrics. The codes are available at https://github.com/XiaoBuL/CM-UNet.