摘要
针对卷积下采样在遥感影像处理中特征提取能力不足,以及传统特征级融合方法未能充分发挥多源遥感数据互补优势的问题,提出结合改进深度可分离卷积与多尺度特征提取模块的多源遥感融合影像目标检测网络.设计双分支可分离卷积模块,通过深度卷积与残差连接增强深层语义特征表达,提升复杂背景下的判别性能.构建全局-局部自适应特征融合模块,利用分离卷积将特征图拆分为不同维度分量,分别捕获全局结构与局部细节,再通过自适应机制进行融合,实现跨源影像信息互补与多尺度特征协同.实验在VEDAI多源数据集上验证,平均检测精度达到82.80%,较ICAfusion提升2.00个百分点,在与YOLOrs、YOLOfusion、SuperYOLO、MF-YOLO等方法对比中保持更优表现.所提网络在多源遥感影像特征级融合方面展现出较高有效性,在目标检测任务中取得显著性能提升.
A multi-source remote sensing image fusion and object detection network based on improved depthwise separable convolution and a multi-scale feature extraction module was proposed to address the limitation of convolutional downsampling in feature extraction and the problem of traditional feature-level fusion methods failing to fully leverage the complementary advantages of multi-source remote sensing data.A dual-branch separable convolution module was designed to enhance deep semantic feature representation through depthwise convolution and residual connections,thereby improving discriminative performance under complex backgrounds.Furthermore,a global-local adaptive feature fusion module was constructed,where feature maps were decomposed into different dimensional components using separable convolution to capture global structures and local details separately.These features were then fused via an adaptive mechanism to achieve cross-source information complementarity and multi-scale feature collaboration.Experiments on the VEDAI multi-source dataset demonstrated that the proposed method achieved a mean average precision(mAP)of 82.80%,which was 2.00 percentage points higher than that of ICAfusion,while also outperforming YOLOrs,YOLOfusion,SuperYOLO,and MF-YOLO.The network shows high effectiveness in featurelevel fusion of multi-source remote sensing images and yields significant performance improvements in object detection tasks.
作者
陈江浩
杨军
CHEN Jianghao;YANG Jun(Faculty of Geomatics,Lanzhou Jiaotong University,Lanzhou 730070,China;National and Local Joint Engineering Research Center of Geographical Monitoring Technology Application,Lanzhou 730070,China;Gansu Provincial Engineering Laboratory of Geographical Monitoring,Lanzhou 730070,China;School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)
出处
《浙江大学学报(工学版)》
北大核心
2025年第12期2545-2555,共11页
Journal of Zhejiang University(Engineering Science)
基金
国家自然科学基金资助项目(42261067)
2025年度甘肃省重点人才资助项目(2025RCXM031).
关键词
多源遥感影像
特征提取
特征级融合
深度可分离卷积
多尺度特征
目标检测
multi-source remote sensing image
feature extraction
feature-level fusion
depthwise separable convolution
multi-scale feature
object detection
