Contrastive Learning Ideas in Underwater Terrain Image Matching

The distinctive nature of the intrinsic features of underwater terrain images, along with their variability of intensity and texture due to the complex environment in which they are acquired, poses great challenges to manual feature-based template-matching methods. In this paper, we propose a novel data-driven framework for template matching of underwater terrain images. This combines the idea of contrastive learning with the template-matching process, allowing self-supervised end-to-end training without the need for additional data annotation. The resulting positive and negative sample comparisons improve the discriminatory ability of the model. Specifically, we achieve data enhancement by simulating grayscale and texture differences between images to improve the anti-interference capability of the model, and we propose a patch-based sample-extraction strategy to extract positive and negative samples. To further improve the discriminative ability for features among patches, we propose a feature-fusion module based on an attention mechanism. This improves the level of discrimination of the features to be matched by fusing the contextual features of patches. In addition, we solve the problem of matching multi-scale templates by introducing a masking mechanism. We performed detailed ablation experiments on the components in the framework to verify their effectiveness. We also compared the performance of our model with other state-of-the-art template-matching methods. The experimental results demonstrate that our proposed method has superior matching performance when compared to its competitors.