Self-supervised multimodal change detection based on difference contrast learning for remote sensing imagery

Xuan Hou, Yunpeng Bai, Yefan Xie, Yunfeng Zhang, Lei Fu, Ying Li, Changjing Shang, Qiang Shen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Most existing change detection (CD) methods target homogeneous images. However, in real-world scenarios like disaster management, where CD is urgent and pre-changed and post-changed images are typical of different modalities, significant challenges arise for multimodal change detection (MCD). One challenge is that bi-temporal image pairs, sourced from distinct sensors, may cause an image domain gap. Another issue surfaces when multimodal bi-temporal image pairs require collaborative input from domain experts who are specialised among different image fields for pixel-level annotation, resulting in scarce annotated samples. To address these challenges, this paper proposes a novel self-supervised difference contrast learning framework (Self-DCF). This framework facilitates networks training without labelled samples by automatically exploiting the feature information inherent in bi-temporal imagery to supervise each other mutually. Additionally, a Unified Mapping Unit reduces the domain gap between different modal images. The efficiency and robustness of Self-DCF are validated on five popular datasets, outperforming state-of-the-art algorithms.

Original languageEnglish
Article number111148
JournalPattern Recognition
Volume159
DOIs
StatePublished - Mar 2025

Keywords

  • Change detection
  • Multimodal image
  • Remote sensing
  • Self-supervised learning

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