河北工业科技2025，Vol.42Issue(6)：499-509,557,12.DOI:10.7535/hbgykj.2025yx06001
基于多模态遥感图像的特征融合模型
Feature fusion model based on multi-modal remote sensing images
摘要
Abstract
To address the issues such as limited model accuracy and large parameter scale of traditional single-branch networks in semantic segmentation of remote sensing images,a large-kernel convolution-based multi-modal feature fusion network(LMFNet)module was proposed.An improved large-kernel MobileNetV3(GMBNetV3)was adopted as the parallel backbone,and multi-source features were fused through cross-self-attention enhancement module.The gated aggregator was utilized to integrate abstract and texture information in the decoding stage.On the public datasets Potsdam and Vaihingen,LMFNet was compared with current advanced multi-modal image segmentation models in terms of performance,and ablation experiments were conducted to verify the functions of each module of the model.The results show that LMFNet improves the segmentation performance of mIoU by approximately 0.32 percentage points～6.50 percentage points compared to other advanced multi-modal segmentation models,while reducing the parameter quantity by 29.3%～73.6%,and the inference speed is increased by 1.7%～45.9%on the Potsdam dataset.The proposed model effectively fuses the differences in image features and can perform semantic segmentation of remote sensing images more clearly,providing strong support for instance segmentation of remote sensing images in urban management.关键词
计算机神经网络/大核卷积/遥感图像分割/多模态/特征融合Key words
computer neural network/large kernel convolution/remote sensing image segmentation/multi-modal/feature fusion分类
信息技术与安全科学引用本文复制引用
王建霞,仇绍祖,杨春金,吴长莉,张晓明..基于多模态遥感图像的特征融合模型[J].河北工业科技,2025,42(6):499-509,557,12.基金项目
河北省自然科学基金(F2022208002) （F2022208002）
