Bi-Level Meta-Learning for Reliable Remote Sensing Image Registration.
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| Title: | Bi-Level Meta-Learning for Reliable Remote Sensing Image Registration. |
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| Authors: | Shi, Lin1 (AUTHOR), Wang, Renzhen1,2 (AUTHOR), Zhu, Xiaofeng2,3 (AUTHOR), An, Cong1 (AUTHOR), Zhao, Kai1,2 (AUTHOR), Shu, Jun1,3 (AUTHOR), Yang, Dongfang3 (AUTHOR) yangdf@xjtu.edu.cn, Meng, Deyu1 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p2007. 30p. |
| Subjects: | Image registration, Remote sensing, Mathematical optimization, Drone aircraft, Machine learning |
| Abstract: | Highlights: What are the main findings? A saliency-aware bi-level optimization framework is proposed to address the challenges of extreme radiometric and temporal variations in heterogeneous remote sensing image matching. A novel Saliency Judgment module is developed to intelligently prioritize geometrically stable landmarks while suppressing spurious matches in weakly informative regions. What are the implications of the main findings? The bi-level meta-learning strategy establishes a new paradigm for UAV localization by enabling effective model training under weakly labeled data regimes, reducing the reliance on large-scale expert annotations. The framework significantly enhances registration accuracy and reduces ghosting artifacts, providing a robust "quality-over-quantity" correspondence selection strategy for autonomous aerial localization. Unmanned aerial vehicle (UAV) visual navigation relies critically on robust image matching between UAV-acquired aerial imagery and pre-existing satellite reference maps. However, extreme cross-domain heterogeneity—encompassing temporal, radiometric, viewpoint, and sensor variations—causes severe performance degradation in existing deep learning-based matchers trained on conventional benchmarks. Furthermore, manual annotation of ground-truth correspondences is prohibitively expensive. This paper proposes a semi-supervised saliency-aware image matching framework with bi-level meta-learning. Our approach comprises two synergistic stages: (1) automated dense correspondence generation via parameterized geometric synthesis, which constructs a large-scale coarse dataset D c (approximately 50,000 pairs) without dense manual point annotation, serving as the primary training corpus for the feature matching network; (2) expert-validated meta-data curation producing a high-quality meta-dataset D m (500 pairs) that supervises the training of a Saliency Judgment Network through bi-level meta-optimization, enabling the network to identify and prioritize geometrically reliable correspondences. Experimental results on the proposed RS-Hetero-50K benchmark and cross-domain FuJian-Mountain dataset demonstrate substantial improvements over representative sparse and detector-free matchers, including LoFTR, SuperGlue, and LightGlue. The complete CNN-attention and saliency-aware framework achieves 95.4% matching precision, which is consistent with the best result reported in the experimental section. The plug-and-play experiments further confirm that the proposed saliency module consistently improves representative sparse and detector-free matchers, indicating that the performance gain stems from both stronger feature representation and saliency-guided correspondence selection. The largest terrain-specific gain is observed in gobi scenes, where the AUC@5 px improves by 16.8% relative to the LoFTR baseline, demonstrating improved robustness in weakly textured remote sensing environments. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A saliency-aware bi-level optimization framework is proposed to address the challenges of extreme radiometric and temporal variations in heterogeneous remote sensing image matching. A novel Saliency Judgment module is developed to intelligently prioritize geometrically stable landmarks while suppressing spurious matches in weakly informative regions. What are the implications of the main findings? The bi-level meta-learning strategy establishes a new paradigm for UAV localization by enabling effective model training under weakly labeled data regimes, reducing the reliance on large-scale expert annotations. The framework significantly enhances registration accuracy and reduces ghosting artifacts, providing a robust "quality-over-quantity" correspondence selection strategy for autonomous aerial localization. Unmanned aerial vehicle (UAV) visual navigation relies critically on robust image matching between UAV-acquired aerial imagery and pre-existing satellite reference maps. However, extreme cross-domain heterogeneity—encompassing temporal, radiometric, viewpoint, and sensor variations—causes severe performance degradation in existing deep learning-based matchers trained on conventional benchmarks. Furthermore, manual annotation of ground-truth correspondences is prohibitively expensive. This paper proposes a semi-supervised saliency-aware image matching framework with bi-level meta-learning. Our approach comprises two synergistic stages: (1) automated dense correspondence generation via parameterized geometric synthesis, which constructs a large-scale coarse dataset D c (approximately 50,000 pairs) without dense manual point annotation, serving as the primary training corpus for the feature matching network; (2) expert-validated meta-data curation producing a high-quality meta-dataset D m (500 pairs) that supervises the training of a Saliency Judgment Network through bi-level meta-optimization, enabling the network to identify and prioritize geometrically reliable correspondences. Experimental results on the proposed RS-Hetero-50K benchmark and cross-domain FuJian-Mountain dataset demonstrate substantial improvements over representative sparse and detector-free matchers, including LoFTR, SuperGlue, and LightGlue. The complete CNN-attention and saliency-aware framework achieves 95.4% matching precision, which is consistent with the best result reported in the experimental section. The plug-and-play experiments further confirm that the proposed saliency module consistently improves representative sparse and detector-free matchers, indicating that the performance gain stems from both stronger feature representation and saliency-guided correspondence selection. The largest terrain-specific gain is observed in gobi scenes, where the AUC@5 px improves by 16.8% relative to the LoFTR baseline, demonstrating improved robustness in weakly textured remote sensing environments. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18122007 |
