Robust Change Detection Using Channel-Wise co-Attention-Based Siamese Network With Contrastive Loss Function

Eunjeong Choi, Jeongtae Kim

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Change detection methods aim to identify significantly changed areas in co-registered bitemporal images taken of the same area. Since not only do bitemporal images usually have different environmental conditions (i.e., different weather conditions, noises, and seasonal changes) but also changes irrelevant to the purpose of change detection (e.g., road changes when detecting building change), which should not be detected as changed areas, change detection methods often suffer from the problem of pseudo-change detection. To alleviate this problem, we propose an encoder-decoder-based Siamese network with a channel-wise co-attention module that considers the channel-wise correlations between a feature map in one image and all feature maps in the other image. By comparing the feature map in one image with the revised feature map in the other image considering the correlations, we are able to reduce the differences between the feature maps when pseudo-changes exist, thereby rendering the proposed method more robust to pseudo-changes. In addition, we apply a contrastive loss function that encourages the pairs of feature maps corresponding to unchanged regions to be similar, which can help improve the performance of change detection. We verified the performance of the proposed method through experiments using datasets such as the change detection dataset (CDD) and building change detection dataset (BCDD). In the experiment, the proposed method achieved significantly improved performance compared with existing methods in terms of recall, precision, f1-score, and overall accuracy.

Original languageEnglish
Pages (from-to)45365-45374
Number of pages10
JournalIEEE Access
Volume10
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Attention
  • Siamese network
  • change detection
  • co-attention
  • deep learning
  • remote sensing

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