Dual-Branch Network for Spatial–Channel Stream Modeling Based on the State-Space Model for Remote Sensing Image Segmentation

To quickly and effectively address the color similarity issue in remote sensing image segmentation, traditional channel attention methods typically use channel modeling based on global channel statistics mapped to weights. However, this approach either suffers from limitations in feature selection due to a lack of dynamic interactions and the loss of significant spatial information, resulting in poor performance in complex scenarios, or has high computational complexity, making it difficult to apply in high-resolution remote sensing images. To overcome these challenges, this article proposes an innovative streaming channel modeling method based on state-space models (SSMs), aimed at rapidly and efficiently tackling the color similarity problem in remote sensing image segmentation. Specifically, we designed the channel-position state-space model network (CPSSNet) framework, where the decoder comprises the spatial Mamba block (SMB) for spatial modeling and the channel Mamba block (CMB) for streaming channel modeling. The core component of SMB, position-selective-scan-2D, achieves multidirectional global modeling in the spatial domain through a combination of the spatial scanning algorithm and SSM, with linear complexity. The core component of CMB, channel-2D selective-scan (C-SS2D), fuses channel and spatial information into patches for streaming modeling using a combination of the channel scanning (CS) algorithm and SSM. We have further improved SSM within C-SS2D to enhance dynamic interactions between channels, allowing for more refined modeling while maintaining linear complexity. Experimental results demonstrate that CPSSNet exhibits outstanding performance in addressing color similarity challenges in remote sensing image segmentation. The code is available at https://github.com/yysdck/CPSSNet.