DGDE-CSFNet: Difference-Guided Dual-Branch Encoding and Cross-Scale Fusion for Maritime Scene Segmentation

Semantic segmentation of maritime imagery is challenging due to the coexistence of large homogeneous regions, such as sea and land, and small, sparsely distributed ship targets, particularly in complex port environments. Further, strong background dominance and intricate boundary structures often result in missed detections and inaccurate ship delineation with conventional segmentation frameworks. To address these challenges, this paper proposes DGDE-CSFNet, a Difference-Guided Dual-Branch Cross-Fusion Network with Cross-Scale Feature Fusion specifically designed for sea–land–ship segmentation. The network employs a dual-branch encoder in which convolutional and Transformer backbones operate in parallel to capture local spatial structures and long-range contextual dependencies. To exploit their complementary behaviors, the encoding stage incorporates a Hierarchical Difference-Guided Attention (HDGA) module. By explicitly characterizing inter-branch inconsistencies, the module selectively enhances discriminative responses that are essential for accurate small-object perception. The decoder further adopts a Multi-Level Cross-Scale Fusion (MCSF) to aggregate multi-resolution features, enabling effective interaction between shallow spatial details and deep semantic representations. This design improves boundary localization and structural consistency while suppressing background interference and preserving subtle ship details. Experimental results on the ISDSD and HRSC2016-SL datasets demonstrate that our method achieves consistent performance gains over existing state-of-the-art methods.