摘要
针对高分辨率遥感图像中地物背景复杂多样,成像过程中道路区域易受树木、建筑物遮挡影响,从中提取道路时易出现局部断连和细节缺失问题,设计并实现了一种基于多尺度差异聚合机制的道路提取网络模型(MSDANet).网络模型整体采用编码-解码器结构,使用Res2Net模块作为编码器骨干网络获取细粒度多尺度特征信息,增大特征提取感受野;同时结合道路形态特征提出一种门控轴向引导模块,用于突出道路特征的表达,改善道路提取长距离断裂现象;此外,设计了一种应用于编解码器之间的多尺度差异聚合模块,用以提取浅层与深层特征间的差异信息并将其聚合,并通过特征融合模块将聚合特征与解码特征融合,促进解码器准确还原道路特征;在高分辨率遥感数据集DeepGlobe和CHN6-CUG上进行模型实验评估,所提方法的F1值分别为80.37%、78.17%,IoU分别为67.18%、64.17%,均优于对比模型.
In the extraction of roads from high-resolution remote sensing images,problems such as local disconnections and the loss of details are common due to the complex backgrounds and the presence of trees and buildings covering the roads during the image formation process.To solve these problems,this study proposes a road extraction model called MSDANet,based on a multi-scale difference aggregation mechanism.The model has an encoder-decoder structure,using the Res2Net module as the backbone network of the encoder to obtain information with fine-grained and multi-scale features from the images and to expand the receptive field for feature extraction.Additionally,a gated axial guidance module,in conjunction with road morphological features,is applied to highlight the representation of road features and improve the connectivity of long-distance roads in road extraction.Furthermore,a multi-scale difference aggregation module is used between the encoder and decoder to extract and aggregate the different information between shallow and deep features.The aggregated features are then fused with the decoded features through a feature fusion module to facilitate the decoder to accurately restore road features.The proposed method has been evaluated on two high-resolution remote sensing datasets:DeepGlobe and CHN6-CUG.The results show that the F1 score of the MSDANet model is 80.37% and 78.17% respectively,and the IoU is 67.18% and 64.17% respectively.It indicates that the proposed model outperforms the comparison models.
作者
许明
周春晖
姜彦吉
XU Ming;ZHOU Chun-Hui;JIANG Yan-Ji(Software College,Liaoning Technical University,Huludao 125105,China)
出处
《计算机系统应用》
2024年第9期95-104,共10页
Computer Systems & Applications
基金
辽宁省教育厅科学技术研究项目(LJKZ0338)。
