  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.
