Towards Pseudo-Labeling with Dynamic Thresholds for Cross-View Image Geolocalization.
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| Title: | Towards Pseudo-Labeling with Dynamic Thresholds for Cross-View Image Geolocalization. |
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| Authors: | Yuan, Yuanyuan1,2 (AUTHOR), Guo, Jianzhong1,2 (AUTHOR), Zhu, Ruoxin1,3 (AUTHOR), Li, Ning1,2 (AUTHOR) lining@henu.edu.cn, Li, Ziwei1,2 (AUTHOR), Luo, Weiran1,2,3 (AUTHOR) |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 6, p944. 26p. |
| Subjects: | Thresholding algorithms, Location data, Supervised learning, Machine learning |
| Abstract: | Highlights: A cross-view image generation model (BEV-CycleGAN+CL) is proposed, integrating BEV geometric constraints with multi-scale contrastive learning to enhance structural consistency in ground-to-satellite image translation. A dynamic threshold-based pseudo-label self-training method is designed to adaptively balance label quality and quantity, effectively leveraging unlabeled data for improved geo-localization. What are the main findings? BEV-CycleGAN+CL achieves PSNR improvements of 42.30% (CVACT) and 41.45% (CVUSA) over CycleGAN, significantly improving image fidelity and structural consistency. The dynamic threshold pseudo-labeling method attains an R@1 of 70.30% with only 1% labeled data on CVACT, outperforming the state-of-the-art UCVGL method (68.29%). What are the implications of the main findings? This study provides a high-quality, annotation-efficient framework for cross-view geo-localization, supporting applications such as disaster response and autonomous driving. The adaptive pseudo-label selection mechanism effectively leverages unlabeled data, offering a scalable solution for semi-supervised and self-supervised geo-localization research. Cross-view image geolocalization aims to achieve accurate localization of geo-tagged images without geo-tagging by matching ground-view images with satellite images. However, there are huge imaging differences between ground and satellite viewpoints, and existing methods usually rely on a large number of accurately labeled cross-view image pairs. Therefore, to address issues such as significant perspective differences, high annotation costs, and low utilization of unpaired data, this paper proposes a cross-view generation model that integrates multi-scale contrastive learning and dynamic optimization, designs a multi-scale contrast loss function to strengthen the semantic consistency between the generated images and the target domain, adaptively balances the quality and quantity of pseudo-labels according to a dynamic threshold screening mechanism, and introduces a hard-sample triplet loss to enhance the model discriminative ability. Ablation experiments on the CVUSA and CVACT datasets show that the BEV-CycleGAN+CL (Bird's-Eye View Cycle-Consistent Generative Adversarial Network with Contrastive Learning) model proposed in this paper significantly outperforms the comparative models in PSNR, SSIM, and RMSE metrics. Specifically, on the CVACT dataset, compared with the BEV-CycleGAN, BEV, and CycleGAN baselines, PSNR increased by 2.83%, 16.02%, and 42.30%, SSIM increased by 6.12%, 8.00%, and 18.48%, and RMSE decreased by 9.28%, 15.51%, and 25.35%, respectively. Similar advantages are observed on the CVUSA dataset. Compared with current state-of-the-art models, the dynamic threshold pseudo-label localization method in this paper demonstrates overall superiority in recall metrics such as R@1, R@5, R@10, and R@1%, for example achieving an R@1 of 98.94% on CVUSA, outperforming the best comparative model, Sample4G†, which reached 98.68%. This study provides innovative methodological support for disaster emergency response, high-precision map construction for autonomous driving, military reconnaissance, and other applications. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: A cross-view image generation model (BEV-CycleGAN+CL) is proposed, integrating BEV geometric constraints with multi-scale contrastive learning to enhance structural consistency in ground-to-satellite image translation. A dynamic threshold-based pseudo-label self-training method is designed to adaptively balance label quality and quantity, effectively leveraging unlabeled data for improved geo-localization. What are the main findings? BEV-CycleGAN+CL achieves PSNR improvements of 42.30% (CVACT) and 41.45% (CVUSA) over CycleGAN, significantly improving image fidelity and structural consistency. The dynamic threshold pseudo-labeling method attains an R@1 of 70.30% with only 1% labeled data on CVACT, outperforming the state-of-the-art UCVGL method (68.29%). What are the implications of the main findings? This study provides a high-quality, annotation-efficient framework for cross-view geo-localization, supporting applications such as disaster response and autonomous driving. The adaptive pseudo-label selection mechanism effectively leverages unlabeled data, offering a scalable solution for semi-supervised and self-supervised geo-localization research. Cross-view image geolocalization aims to achieve accurate localization of geo-tagged images without geo-tagging by matching ground-view images with satellite images. However, there are huge imaging differences between ground and satellite viewpoints, and existing methods usually rely on a large number of accurately labeled cross-view image pairs. Therefore, to address issues such as significant perspective differences, high annotation costs, and low utilization of unpaired data, this paper proposes a cross-view generation model that integrates multi-scale contrastive learning and dynamic optimization, designs a multi-scale contrast loss function to strengthen the semantic consistency between the generated images and the target domain, adaptively balances the quality and quantity of pseudo-labels according to a dynamic threshold screening mechanism, and introduces a hard-sample triplet loss to enhance the model discriminative ability. Ablation experiments on the CVUSA and CVACT datasets show that the BEV-CycleGAN+CL (Bird's-Eye View Cycle-Consistent Generative Adversarial Network with Contrastive Learning) model proposed in this paper significantly outperforms the comparative models in PSNR, SSIM, and RMSE metrics. Specifically, on the CVACT dataset, compared with the BEV-CycleGAN, BEV, and CycleGAN baselines, PSNR increased by 2.83%, 16.02%, and 42.30%, SSIM increased by 6.12%, 8.00%, and 18.48%, and RMSE decreased by 9.28%, 15.51%, and 25.35%, respectively. Similar advantages are observed on the CVUSA dataset. Compared with current state-of-the-art models, the dynamic threshold pseudo-label localization method in this paper demonstrates overall superiority in recall metrics such as R@1, R@5, R@10, and R@1%, for example achieving an R@1 of 98.94% on CVUSA, outperforming the best comparative model, Sample4G†, which reached 98.68%. This study provides innovative methodological support for disaster emergency response, high-precision map construction for autonomous driving, military reconnaissance, and other applications. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18060944 |
