CM-UNet: Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentation
This work addresses the problem of efficient semantic segmentation for remote sensing images, which is incremental as it builds on existing CNN and Transformer methods by integrating Mamba for better global modeling.
The paper tackles remote sensing image semantic segmentation by proposing CM-UNet, a hybrid CNN-Mamba model that combines a CNN encoder with a Mamba-based decoder to capture long-range dependencies and multi-scale context, achieving improved performance on three benchmarks.
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.