Cyclic Consistency Constrained Multiview Graph Matching Network for Unsupervised Heterogeneous Change Detection
Jiahui Qu, Wenqian Dong, Qian Du, Yufei Yang, Yunshuang Xu, Yunsong Li
Abstract
Change detection of heterogeneous remote sensing images is a crucial topic for Earth observation, which has various applications in many fields. Most of the existing heterogeneous change detection methods obtain modal-consistent feature representation without fully considering the characteristic of specific data modality, such as hyperspectral image (HSI). Moreover, the acquirement of labeled samples requires high costs of manual operation and extensive domain knowledge. To solve these problems, we propose a cyclic consistency constrained multi-view graph matching network (C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> MGM-Net) for unsupervised change detection, which fully considers the spatial-spectral similarity of heterogeneous multi-temporal images from multiple views while preventing the information loss of HSI and PAN/RGB image. The C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> MGM-Net transforms the heterogeneous images into three common domains for modal alignment, which not only enhances the spatial-spectral information, but also well preserves the original high-resolution spatial and spectral information in the multi-temporal images. The modal-consistent spatial and spectral information is interacted between multiple domains, so as to make the difference features more distinguishable in terms of both structural and node similarity. With the guidance of change detection results in all domains, the most informative samples are intelligently selected to enlarge the training set, and then fed back to further constrain the consistency of unchanged areas of the multi-temporal images in each domain. The experimental results on heterogeneous datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods. Code is available at https://github.com/Jiahuiqu/C3MGM-for-Heterogeneous-Change-Detection.