计算机工程与应用2024，Vol.60Issue(15)：234-242,9.DOI:10.3778/j.issn.1002-8331.2311-0400
多模态融合的遥感图像语义分割网络
Remote Sensing Image Semantic Segmentation Network Based on Multimodal Fusion
摘要
Abstract
Multimodal semantic segmentation networks can utilize complementary data in different modalities to improve segmentation accuracy.However,existing multimodal semantic segmentation models often combine two types of modal data with simple way,neglecting the information features in the high and low frequencies of different modal data,leading to insufficient cross-modal feature extraction and suboptimal fusion.To address these issues,this paper proposes a remote sensing image semantic segmentation network,LHFNet(low feature and high feature fusion network),which fuses IRRG images with DSM images.Firstly,for the structure features related to the low frequency of each modal image,a low-level feature extraction enhancement module is designed to strengthen the extraction of different modal features.Secondly,based on the detail features that are independent of each other in the high frequency of each modal image,a high-level fea-ture fusion module is designed to guide the fusion of different modal features.Finally,for the semantic gap between the high and low frequency image features,a global atrous spatial pyramid pooling module is designed to skip the connection of high and low frequency information,to enhance the information interaction between the high and low frequency image features.Experiments on the Vaihingen and Potsdam datasets provided by ISPRS show that LHFNet has achieved global accuracies of 88.17%and 90.53%respectively,which are higher than the segmentation accuracies of single-mode segmen-tation networks such as SegNet,DeepLabv3+,and multimodal RGB-D segmentation networks such as RedNet,TSNet.关键词
多模语义分割/特征融合/特征提取/多源遥感图像Key words
multimodal semantic segmentation/feature fusion/feature extraction/multi-source remote sensing images分类
信息技术与安全科学引用本文复制引用
胡宇翔,余长宏,高明..多模态融合的遥感图像语义分割网络[J].计算机工程与应用,2024,60(15):234-242,9.基金项目
浙江省自然科学基金(LY22F010013) （LY22F010013）
宁波市自然科学基金重点项目(2022J067). （2022J067）
