Remote sensing image semantic segmentation, as the core technology of geographic information interpretation, achieves precise extraction of ground objects through pixel-level classification, and has important application value in fields such as land planning and environmental monitoring. However, existing methods generally face dual challenges of edge blurring and multi-scale target representation conflicts when processing high-resolution images: traditional convolutional networks are limited by local receptive fields leading to discontinuous boundaries, while mainstream Transformer architectures have difficulty balancing detail preservation and multi-scale modeling due to high computational complexity. To address these issues, this study proposes an edge-enhanced segmentation framework EMambaEdgeNet, innovatively constructing a three-stream feature decoupling architecture. Inheriting the global-local feature modeling advantages of RS3Mamba, it breaks through the perceptual bottleneck of traditional dual-branch models by introducing an edge feature enhancement branch. The core innovations of this method include: a multi-level feature erasure-residual reconstruction module based on reverse attention mechanism to enhance features in high-detail regions; a multi-directional geometric perception module combining dynamic separable convolution and rotation-invariant gradient operators to improve complex boundary direction discrimination; and a bidirectional cross-layer fusion mechanism with channel-spatial dual attention gating to promote dynamic complementarity of multi-scale features. Experiments on ISPRS Potsdam and Vaihingen datasets demonstrate the effectiveness of our proposed model in edge preservation and multi-scale target segmentation in complex scenarios. To our knowledge, this is the first three-stream feature decoupling model for remote sensing image semantic segmentation.