遥感图像跨域语义分割的无监督域自适应对齐方法

doi:10.11947/j.AGCS.2023.20220483

摘要/Abstract

摘要： 深度学习模型依赖于大量同源标记样本,即限定训练数据与测试数据服从同一分布。但是,面对大范围、多样化的遥感图像时,数据之间的同分布要求难以保证,深度学习模型的分割精度下降明显。针对这一问题,本文提出了一种面向遥感图像语义分割的无监督域自适应方法,在训练数据(源域)与测试数据(目标域)分布不同的情况下,仅利用源域标签训练深度学习模型,提高目标域语义分割精度。本文方法引入最优传输理论,在图像空间、特征空间和输出空间进行全局对齐,以减轻源域和目标域之间的域偏移。试验采用国际摄影测量与遥感学会(ISPRS)所提供的Potsdam和Vaihingen数据集进行验证。结果表明,相比于现有方法,本文方法取得了更高的分割精度。此外,通过消融分析,在深度学习驱动的语义分割无监督域自适应框架下,证明了最优传输理论的有效性。

关键词: 遥感图像, 语义分割, 域自适应, 最优传输

Abstract: Deep learning models rely on a large number of homogenous labeled samples, i.e., limiting the training and testing data to obey the same data distribution. However, when facing large-scale and diverse remote sensing data, it is difficult to guarantee the requirement of homogeneous distribution among data, and the segmentation accuracy of deep learning models decreases significantly. To address this problem, this paper proposes an unsupervised domain adaptation (UDA) method for semantic segmentation of remote sensing images. When the distributions of training data (source domain) and testing data (target domain) are different, the proposed method improves the accuracy of semantic segmentation in the target domain by training deep learning models using only source-domain labels. The method introduces optimal transport theory and global alignment in image, feature and output spaces to mitigate the domain shift between the source and target domains. The experiments employ the Potsdam and Vaihingen datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) to validate the performance. The results show that the method in this paper achieves higher accuracy compared with existing methods. Based on the ablation study, the effectiveness of the optimal transport theory is demonstrated in the UDA framework for semantic segmentation driven by deep learning.

Key words: remote sensing imagery, semantic segmentation, domain adaptation, optimal transport

中图分类号:

P237

沈秭扬, 倪欢, 管海燕. 遥感图像跨域语义分割的无监督域自适应对齐方法[J]. 测绘学报, 2023, 52(12): 2115-2126.

SHEN Ziyang, NI Huan, GUAN Haiyan. Unsupervised domain adaptation alignment method for cross-domain semantic segmentation of remote sensing images[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(12): 2115-2126.

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