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

陈昊; 周光尧; 王乾通; 高斌; 王文志; 唐皓

doi:10.11999/JEIT240720

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

doi: 10.11999/JEIT240720

陈昊¹,
周光尧^2, ,,
王乾通²,
高斌²,
王文志²,
唐皓²

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

详细信息

作者简介:
陈昊：男，副研究员，研究方向为遥感图像处理，变化检测

周光尧：男，研究员，研究方向为遥感信息处理系统，遥感图像解译

王乾通：男，助理研究员，研究方向为光学遥感影像解译

高斌：男，助理研究员，研究方向为遥感信息处理系统

王文志：男，助理研究员，研究方向为遥感信息处理系统，图像解译，多源信息融合处理

唐皓：男，高级工程师，研究方向为遥感信息处理系统，高光谱图像处理，目标智能解译

通讯作者:
周光尧　zhougy@aircas.an.cn

中图分类号: TN991.73; TP751.2
计量
- 文章访问数: 94
- HTML全文浏览量: 41
- PDF下载量: 14
- 被引次数: 0
出版历程
- 收稿日期: 2024-08-19
- 修回日期: 2025-01-16
- 网络出版日期: 2025-02-24

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

1.
Beijing Institute of Tracking and Communication Technology, Beijing 100094, China
2.
Aerospace Information Innovation Research Institute, Chinese Academy of Sciences, Beijing 100094, China

摘要

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。
- 多时相遥感 /
- 建筑物变化检测 /
- 对抗生成网络 /
- 数据对生成
Abstract: 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 for preserving the integrity of the generated data and ensuring its relevance to real-world scenarios. The network is composed of two main components: a generator and a discriminator, which work in tandem through an adversarial learning process. The generator aims to produce realistic and semantically consistent multi-temporal image pairs, while the discriminator evaluates the quality of the generated data, guiding the generator to improve iteratively. Additionally, BAG-GAN is equipped with multimodal output capabilities, enabling the generation of diverse building change data pairs. This diversity enhances the robustness of deep learning models by exposing them to a wider range of scenarios during training. To address the issue of limited training data, the study incorporates a data augmentation strategy. Original datasets, such as LEVIR-CD and WHU-CD, were reorganized by combining change labels with multi-temporal remote sensing images to create new synthetic datasets. These augmented datasets, along with the data generated by BAG-GAN, were used to train and evaluate several widely recognized deep learning models, including FC-EF, FC-Siam-Conc, and others. Comparative experiments were conducted to assess the effectiveness of BAG-GAN and its contribution to improving model performance in multi-temporal building change detection. Results and Discussions The experimental results demonstrate that BAG-GAN effectively addresses the challenges of insufficient labeled data in building change detection tasks. Models trained on the augmented datasets, which included BAG-GAN-generated data, achieved significant improvements in detection accuracy and robustness. For instance, classic models like FC-EF and FC-Siam-Conc showed substantial performance gains when trained on augmented datasets compared to their performance on the original datasets. These improvements validate the effectiveness of BAG-GAN in generating high-quality training data. BAG-GAN also excelled in producing diverse and multimodal building change data pairs Visual comparisons between the generated data and the original datasets highlighted the network’s ability to create realistic and varied data, effectively enhancing the diversity of training datasets. This diversity is critical for addressing the imbalance in existing datasets, where effective building change information is underrepresented. By increasing the proportion of relevant change information in the training data, BAG-GAN improves the learning conditions for deep learning models, enabling them to better generalize across different scenarios. Further analysis revealed that BAG-GAN significantly enhances the ability of detection models to localize changes and recover fine-grained details of building modifications. This is particularly evident in complex scenarios involving subtle or small-scale changes. The adversarial consistency loss function played a pivotal role in ensuring the semantic relevance of the generated data, making BAG-GAN a reliable tool for data augmentation in remote sensing applications. Moreover, the network's ability to generate data pairs with high-quality and multimodal characteristics ensures its applicability to a wide range of remote sensing tasks beyond building change detection. Conclusions This study introduces BAG-GAN, a novel multi-temporal building change detection data pair generation network designed to overcome the limitations of insufficient labeled data in remote sensing. The network incorporates an adversarial consistency loss function, which ensures that the generated data is both semantically consistent and diverse. By leveraging a consistency adversarial generation mechanism, BAG-GAN enhances the quality and diversity of training datasets, addressing key bottlenecks in multi-temporal building change detection tasks. Through experiments on the LEVIR-CD and WHU-CD datasets, BAG-GAN demonstrated its ability to significantly improve the performance of classic remote sensing change detection models, such as FC-EF and FC-Siam-Conc. The results highlight the network’s effectiveness in generating high-quality data pairs that enhance model training and detection accuracy. This research not only provides a robust methodological framework for improving multi-temporal building change detection but also offers a foundational tool for broader applications in remote sensing. The findings pave the way for future advancements in change detection techniques, offering valuable insights for researchers and practitioners in the field.
- Multi-temporal remote sensing /
- Change detection /
- Adversarial Generative Network (AGN) /
- Data pair generation

HTML全文

图 1 本文所提一致性生成对抗BAG-GAN的数据生成方法

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图 2 循环一致性和对抗一致性对比原理

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图 3 本文所提BAG-GAN变化检测数据生成可视化结果

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图 4 LEVIR-CD

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图 5 WHU-CD

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图 6 CD模型性能随数据集类不平衡率变化折线图

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表 1 WHU-CD模型性能提升(20%与100%)

变化检测模型	LEVIR-CD(20%) Prec/Rec/IoU	LEVIR-CD(100%) Prec/Rec/IoU
FC-EF +数据增强变换	0.689/0.696/0.595 0.683/0.654/0.511	0.769/0.682/0.620 0.771/0.665/0.593
+BAG-GAN	0.863/0.641/0.611	0.875/0.757/0.701
FC-Siam-Conc +数据增强变换	0.615/0.709/0.541 0.609/0.698/0.531	0.696/0.802/0.628 0.667/0.735/0.609
+BAG-GAN	0.894/0.711/0.668	0.922/0.741/0.691
FC-Siam-Diff +数据增强变换	0.581/0.690/0.495 0.573/0.634/0.487	0.654/0.787/0.586 0.647/0.772/0.557
+BAG-GAN	0.866/0.618/0.564	0.889/0.781/0.737
SNUNet +数据增强变换	0.940/0.916/0.872 0.913/0.920/0.865	0.956/0.951/0.914 0.903/0.944/0.887
+BAG-GAN	0.933/0.938/0.876	0.961/0.958/0.924

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表 2 LEVIR-CD模型性能提升(20%与100%)

变化检测模型	LEVIR-CD(20%) Prec/Rec/IoU	LEVIR-CD(100%) Prec/Rec/IoU
FC-EF +数据增强变换	0.478/0.482/0.423 0.534/0.552/0.443	0.429/0.331/0.216 0.632/0.441/0.433
+BAG-GAN	0.868/0.696/0.642	0.739/0.649/0.576
FC-Siam-Conc +数据增强变换	0.872/0.574/0.523 0.843/0.611/0.575	0.769/0.625/0.563 0.766/0.613/0.589
+BAG-GAN	0.809/0.711/0.640	0.831/0.732/0.663
FC-Siam-Diff +数据增强变换	0.905/0.617/0.572 0.833/0.623/0.534	0.715/0.693/0.598 0.745/0.693/0.619
+BAG-GAN	0.683/0.749/0.589	0.855/0.753/0.688
SNUNet +数据增强变换	0.857/0.879/0.786 0.887/0.786/0.799	0.935/0.919/0.871 0.913/0.921/0.867
+BAG-GAN	0.927/0.902/0.848	0.959/0.947/0.913

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