湖北民族大学学报（自然科学版）2026，Vol.44Issue(2)：186-191,6.DOI:10.13501/j.cnki.42-1908/n.2026.06.002
融合局部感知与自适应门控的HGA-Mamba遥感图像语义分割模型
HGA-Mamba Semantic Segmentation Model for Remote Sensing Images Fusing Local Perception and Adaptive Gating
摘要
Abstract
To address the challenges in semantic segmentation of high-resolution remote sensing images,such as blurred edge details,missed detection of small objects,and difficulties in multi-scale feature fusion,and to overcome the limitations of the original Mamba architecture in processing complex urban-rural scenes,a hybrid gated attention Mamba(HGA-Mamba)remote sensing image semantic segmentation model fusing local perception and adaptive gating was proposed.First,a hybrid feature encoding(Hybrid)module was constructed by introducing a parallel convolution branch to enhance the model's ability to capture textures and edges,thereby improving boundary segmentation effects.Second,an adaptive gated fusion(AGF)module was designed to dynamically adjust the weights of global and local features based on spatial content,mitigating feature conflicts between large-scale and small-scale objects.Finally,a squeeze-and-excitation(SE)channel attention mechanism was embedded to filter key features and suppress background noise.The results showed that the mean intersection over union(mIoU)of HGA-Mamba model reached 42.10%,increasing by 5.16 percentage points compared to the original visual Mamba(VMamba)model.The pixel accuracy improved by 3.48 percentage points,and the mean pixel accuracy improved by 4.67 percentage points.HGA-Mamba model effectively enhanced convergence speed and training stability,solved the problem of detail loss in complex scenes,and provided technical support for refined segmentation of high-resolution remote sensing images.关键词
状态空间模型/语义分割/高分辨率影像/卷积神经网络/注意力机制/特征融合Key words
state space model/semantic segmentation/high-resolution imagery/convolutional neural network/attention mechanism/feature fusion分类
信息技术与安全科学引用本文复制引用
王泓钧,黎远松,廖婉婷,石睿..融合局部感知与自适应门控的HGA-Mamba遥感图像语义分割模型[J].湖北民族大学学报（自然科学版）,2026,44(2):186-191,6.基金项目
国家自然科学基金项目(42374227,42074218). （42374227,42074218）
