Unsupervised domain adaptation for remote sensing image segmentation based on adversarial learning and self-training | |
Liang Chenbin; Cheng Bo; Xiao Baihua | |
发表期刊 | IEEE Geoscience and Remote Sensing Letters |
2023-05 | |
页码 | 1 |
摘要 | There is a large amount of out-of-distribution data (OOD) in remote sensing, which hinders high-accuracy segmentation models under the assumption of independent identical distribution (i.i.d.) from stable and reliable performance in real-world remote sensing applications. And Domain Adaptation (DA) is presented to seamlessly extend classifiers to the label-scarce target domain in the presence of the label-sufficient source domain with different data distributions. However, given that the domain shift, i.e. the distribution difference between the two domains, is more serious in remote sensing images, the current DA methods for image segmentation in Computer Vision (CV) typically perform unsatisfactorily in remote sensing, even suffering from the negative domain alignment. To this end, this paper proposes the Self-Training Adversarial Domain Adaptation (STADA) method for remote sensing image segmentation, which not only performs adversarial learning to extract domain-invariant features, but also implements Self-Training using pseudo-labels in the target domain denoised by the conditional adversarial loss for classifier adaptation. The ISPRS and WHU datasets are employed to conduct extensive experiments to investigate the effectiveness of STADA and the specific effect of its each DA component. And the experimental results demonstrate that STADA outperforms other state-of-the-art DA methods in the remote sensing image segmentation task. |
收录类别 | SCI |
WOS记录号 | WOS:001004273900004 |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51714 |
专题 | 复杂系统管理与控制国家重点实验室_影像分析与机器视觉 |
推荐引用方式 GB/T 7714 | Liang Chenbin,Cheng Bo,Xiao Baihua. Unsupervised domain adaptation for remote sensing image segmentation based on adversarial learning and self-training[J]. IEEE Geoscience and Remote Sensing Letters,2023:1. |
APA | Liang Chenbin,Cheng Bo,&Xiao Baihua.(2023).Unsupervised domain adaptation for remote sensing image segmentation based on adversarial learning and self-training.IEEE Geoscience and Remote Sensing Letters,1. |
MLA | Liang Chenbin,et al."Unsupervised domain adaptation for remote sensing image segmentation based on adversarial learning and self-training".IEEE Geoscience and Remote Sensing Letters (2023):1. |
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