Image Segmentation Method Based on a Dual-Domain Coupled 2-Additive Fuzzy-Measure Capacity Network

High-resolution remote sensing images exhibit large variations in object scale, ambiguous boundary transitions, and complex interactions among adjacent land-cover regions. Under sparse pixel supervision, these factors may cause object fragmentation, erroneous merging, and boundary displacement. To address these issues, a Dual-Domain Coupled 2-Additive Fuzzy Measure Network (DC2CNet) is proposed for sparse pixel-supervised remote sensing image segmentation. The proposed method extends pixel-level class prediction by jointly modeling regional consistency and boundary discrimination. A region-domain branch and a boundary-domain branch are constructed to characterize intra-region coherence and class-transition information, respectively. A dual-domain coupling mechanism is further introduced to integrate regional and boundary evidence. In addition, a 2-additive fuzzy measure decoding strategy is developed to model individual evidence contributions and pairwise evidence interactions through Möbius representation. During training, sparse pixel annotations are used to generate boundary discrimination probabilities, and neighborhood consistency constraints are introduced to optimize the boundary branch. Experimental results demonstrate that DC2CNet effectively reduces boundary ambiguity and region confusion, while maintaining accurate segmentation of small objects, elongated structures, and complex interface regions.