基于一致性生成对抗的遥感多时相建筑物变化检测数据对生成技术  

Building Change Detection Data Generation Technology for Multi-temporal Remote Sensing Imagery Based on Consistent Generative Adversarial

作　　者：陈昊 周光尧 王乾通 高斌 王文志 唐皓 CHEN Hao;ZHOU Guangyao;WANG Qiantong;GAO Bin;WANG Wenzhi;TANG Hao(Beijing Institute of Tracking and Communication Technology,Beijing 100094,China;Aerospace Information Innovation Research Institute,Chinese Academy of Sciences,Beijing 100094,China)

机构地区：[1]北京跟踪与通信技术研究所,北京100094 [2]中国科学院空天信息创新研究院,北京100094

出　　处：《电子与信息学报》2025年第3期825-838,共14页Journal of Electronics & Information Technology

摘　　要：虽然目前可以获取海量的多时相遥感数据,但是由于建筑物变化时间周期过长,难以获取充足的建筑物变化数据对来支撑数据驱动的深度学习变化检测模型构建,呈现多时相遥感建筑物变化检测处理精度差的问题。因此,为提升变化检测算法模型处理性能,该文从建筑物变化检测训练数据对生成开展研究,基于一致性对抗生成机理提出了多时相建筑物变化检测数据对生成网络(BAG-GAN)。其主要在多时相图像生成过程中采用对抗一致性损失函数约束,在保证生成图像和输入图像关联性的同时,保证了生成模型的多模态输出能力。此外,还通过重组原数据集中的变化标签和多时相遥感图像来进一步提升建筑物变化信息生成的多样性,解决了训练数据中有效建筑物变化信息占比少的问题,为变化监测算法模型的充分训练奠定了基础。最后,在LEVIR-CD和WHU-CD建筑物变化检测数据集上进行了数据生成实验,并使用生成扩充后的数据集训练了多种较为经典的遥感图像变化检测模型,实验结果表明该文提出的BAG-GAN多时相建筑物变化检测数据对生成网络及相应的生成策略可以有效提升变化检测模型的处理精度。Objective Building change detection is an essential task in urban planning,disaster management,environmental monitoring,and other critical applications.Advances in multi-temporal remote sensing technology have provided vast amounts of data,enabling the monitoring of changes over large geographic areas and extended time frames.Despite this,significant challenges persist,particularly in acquiring sufficient labeled data pairs for training deep learning models.Building changes are typically characterized by long temporal cycles,leading to a scarcity of annotated data that is critical for training data-driven deep learning models.This scarcity severely limits the models'capacity to generalize and achieve high accuracy,particularly in complex and diverse scenarios.The performance of existing methods often suffers from poor generalization due to insufficient training data,reducing their applicability to practical tasks.To address these challenges,this study proposes a novel solution:the development of a multi-temporal building change detection data pair generation network,referred to as BAG-GAN.This network leverages a consistency adversarial generation mechanism to create diverse and semantically consistent data pairs.The aim is to enrich training datasets,thereby enhancing the learning capacity of deep learning models for detecting building changes.By addressing the bottleneck of insufficient labeled data,BAG-GAN provides a new pathway for improving the accuracy and robustness of multi-temporal building change detection.Methods BAG-GAN integrates Generative Adversarial Networks(GANs)with a specially designed consistency constraint mechanism,tailored for the generation of data pairs in multi-temporal building change detection tasks.The core innovation of this network lies in its adversarial consistency loss function.This loss function ensures that the generated images maintain semantic consistency with the corresponding input images while reflecting realistic and diverse changes.The consistency constraint is crucial f

关 键 词：多时相遥感 建筑物变化检测 对抗生成网络 数据对生成 

分 类 号：TN991.73[电子电信—信号与信息处理] TP751.2[电子电信—信息与通信工程]