自然资源遥感2025，Vol.37Issue(5)：152-161,10.DOI:10.6046/zrzyyg.2024208
基于全色-多光谱双流卷积网络的端到端地物分类方法
End-to-end land cover classification based on panchromatic-multispectral dual-stream convolutional network
摘要
Abstract
Multispectral(MS)and panchromatic(PAN)images serve as primary data sources for visible-near-infrared optical remote sensing imagery.In a typical land cover classification workflow,the spatial resolution of MS images is generally enhanced using pixel-level fusion methods,followed by image classification.However,the pixel-level fusion process is characterized by considerable time consumption and inconsistency with the optimization objectives of land cover classification,failing to meet the demand for end-to-end remote sensing image classification.To address these challenges,this paper proposed a dual-stream fully convolutional neural network,DSEUNet,which obviates the need for pixel-level fusion.Specifically,two branches were constructed based on the EfficientNet-B3 network to extract features from PAN and MS images,respectively.It was followed by feature-level fusion and decoding,thus outputting the ultimate classification results.Considering that PAN and MS images focus on different features of land cover elements,a spatial attention mechanism was incorporated in the PAN branch to enhance the perception of spatial information,such as details and edges.Moreover,a channel attention mechanism was incorporated in the MS branch to improve the perception of reflectance differences across multiple bands.Experiments on the 10-meter land cover dataset and ablation studies of the network structure demonstrate that the proposed network exhibited higher classification accuracy and faster inference speed.With the same backbone network,DSEUNet outperformed traditional pixel-level fusion-based classification methods,with an increase of 1.62 percentage points in mIoU,1.36 percentage points in mFscore,and 1.49 percentage points in Kappa coefficient,as well as a 17.69%improvement in inference speed.关键词
地物分类/深度学习/双流网络/全色影像/多光谱影像Key words
land cover classification/deep learning/dual-stream network/panchromatic(PAN)image/multi-spectral(MS)image分类
信息技术与安全科学引用本文复制引用
李英龙,邓毓弸,孔赟珑,陈静波,孟瑜,刘帝佑..基于全色-多光谱双流卷积网络的端到端地物分类方法[J].自然资源遥感,2025,37(5):152-161,10.基金项目
国家重点研发计划课题"开放式遥感智能解译平台"(编号:2021YFB3900504)资助. （编号:2021YFB3900504）
