海南大学学报（自然科学版）2025，Vol.43Issue(3)：305-317,13.DOI:10.15886/j.cnki.hdxbzkb.2023121401
聚合双流特征的高分遥感影像场景分类模型
A high-resolution remote sensing image scene classification model with aggregated dual-stream features
摘要
Abstract
Aimed at the limitation of the discriminative capability of the extracted features and unsatisfactory classification performance of the scene classification methods based on the deep learning,in the report,in order to improve the effective learning of the scene-representative features,a dual-stream architecture named SC-ETNet,which include the convolution stream and the transformation stream,was proposed for the remote sensing image scene classification.The convolution stream employed the spatial and channel reconstruction convolutions to separate and reconstruct features extracted by convolutional layers.The transformation stream used LightViT for the interaction between global tokens and image tokens to achieve local-global attention computation.The mean classification accuracy of the evaluation on the UC-Merced,AID,and NWPU-RESISC45 datasets was 99.61%,97.81%,and 95.33%,respectively.These data suggested that compared with the existing advanced scene classification methods,SC-ETNet demonstrates superior classification performance.关键词
高分辨率遥感影像/场景分类/卷积神经网络/视觉转换器Key words
high-resolution remote sensing images/scene classification/convolutional neural network/visual transformation分类
信息技术与安全科学引用本文复制引用
潘凯祥,徐伟铭,何小英,李紫微,王娟..聚合双流特征的高分遥感影像场景分类模型[J].海南大学学报（自然科学版）,2025,43(3):305-317,13.基金项目
国家自然科学基金项目(41801324) （41801324）
国家重点研发计划项目(2017YFB0503505-6) （2017YFB0503505-6）
福建省科技厅引导性项目(2022H0009) （2022H0009）
