基于特征金字塔的协作式变化检测网络  

A Collaborative Change Detection Network Based on Feature Pyramids

作　　者：吕宁[1] 刘亦高 张增辉 LYU Ning;LIU Yigao;ZHANG Zenghui(Xidian University,Xi'an 710000,China)

机构地区：[1]西安电子科技大学,西安710000

出　　处：《遥测遥控》2024年第5期120-128,共9页Journal of Telemetry,Tracking and Command

摘　　要：基于深度学习的变化检测方法在高分辨率遥感图像的应用越来越多。然而,在处理大尺寸遥感图像上,为适应GPU(图形处理器)内存限制而采用的降采样和裁剪策略,往往会导致语义信息不完整和图像细节丢失。本文提出了一种基于特征金字塔的协作式监督网络,使网络能够从裁剪和降采样的图像块中学习局部和整体特征。此外,还引入了一种特征共享机制来融合整体特征和局部特征。在LEVIR-CD(遥感变化检测数据集)和S2Looking(建筑物变化检测数据集)上对该网络进行了评估,并将其与一些代表性的变化检测网络进行了比较。对比实验表明:所提出的网络在多尺度变化检测方面表现更好,在LEVIR-CD数据集上精确率提高了2.69%,在S2Looking数据集上精确率和召回率分别提高了6.83%、2.68%。There are more and more applications of change detection methods based on deep learning in high-resolution remote sensing images.However,downsampling and cropping strategies deployed to fit the GPU(Graphic Processing Unit)memory constraints on processing large-size remote sensing images often result in incomplete semantic information and loss of fine details.In this paper,a collaborative supervised network based on feature pyramids is proposed to enable the network to learn local and overall features from cropped and downsampled image blocks.In addition,a feature-sharing mechanism is introduced to fuse global features and local features.We evaluated the network on the LEVIR-CD(a remote sensing change detection dataset)and S2Looking(a building change detection dataset)by comparing it with some representative change detection networks.The comparison experiments show that the proposed network performs better in multi-scale change detection,with a 2.69%improvement in precision on LEVIR-CD,and 6.83%and 2.68%improvement in precision and recall on the S2Looking dataset,respectively.

关 键 词：变化检测 遥感卫星影像 特征金字塔 特征共享 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]