DGR-MAE: Posterior Semantic Correction Masked Autoencoder for Fine-Grained Aircraft Recognition Under Cloud Occlusion.
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| Title: | DGR-MAE: Posterior Semantic Correction Masked Autoencoder for Fine-Grained Aircraft Recognition Under Cloud Occlusion. |
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| Authors: | Liu, Cong1 (AUTHOR), Gao, Quanwei2 (AUTHOR), Song, Chenxi1,2 (AUTHOR), Ouyang, Bo1,2 (AUTHOR), Wang, Ruyu1 (AUTHOR) wangruyu@nwafu.edu.cn, Fan, Hongtao1 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1852. 29p. |
| Subjects: | Autoencoders, Object recognition algorithms, Remote sensing, Deep learning, Image reconstruction |
| Abstract: | Highlights: What are the main findings? A Differentiated Guided Reconstruction Masked Autoencoder (DGR-MAE) is proposed for cloud-occluded aircraft recognition by incorporating global attention scoring and posterior semantic correction into masked image modeling. DGR-MAE achieves the best performance among the evaluated self-supervised learning methods on the ASRAir benchmark, reaching 74.28% Top-1 accuracy without increasing inference complexity. What are the implications of the main findings? Robust semantic representation learning can improve recognition robustness under cloud-induced information loss by directly learning from incomplete observations. The findings highlight the potential of representation learning-based approaches for remote sensing target recognition under cloud occlusion. Fine-grained aircraft recognition in optical remote sensing imagery remains highly challenging under cloud occlusion, as visibility degradation causes structural information loss and weakens discriminative representation learning. To address this issue, we propose DGR-MAE, a teacher–student masked image modeling framework based on posterior semantic correction for robust representation learning under incomplete observations. Unlike existing semantic-guided masking methods that modify token visibility during input construction, DGR-MAE preserves high-ratio stochastic masking in the student branch and introduces semantic correction after visibility degradation through teacher-guided differential reconstruction. Specifically, a semantic-aware teacher branch estimates patch-level importance to partition masked regions into semantic-critical and non-critical subsets, enabling region-dependent reconstruction prioritization. A collaborative feature refinement mechanism is further incorporated to enhance contextual consistency and structural reasoning during pretraining. To support controlled evaluation, we construct the ASRAir benchmark with hierarchical cloud occlusion levels. Experimental results show that DGR-MAE achieves 74.28% Top-1 accuracy on ASRAir-Occ and achieves the best Top-1 performance while maintaining competitive Top-5 accuracy compared with representative self-supervised baselines. In particular, it demonstrates substantially improved robustness under moderate-to-severe cloud occlusion, validating the effectiveness of posterior semantic correction for remote sensing representation learning under visibility degradation. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A Differentiated Guided Reconstruction Masked Autoencoder (DGR-MAE) is proposed for cloud-occluded aircraft recognition by incorporating global attention scoring and posterior semantic correction into masked image modeling. DGR-MAE achieves the best performance among the evaluated self-supervised learning methods on the ASRAir benchmark, reaching 74.28% Top-1 accuracy without increasing inference complexity. What are the implications of the main findings? Robust semantic representation learning can improve recognition robustness under cloud-induced information loss by directly learning from incomplete observations. The findings highlight the potential of representation learning-based approaches for remote sensing target recognition under cloud occlusion. Fine-grained aircraft recognition in optical remote sensing imagery remains highly challenging under cloud occlusion, as visibility degradation causes structural information loss and weakens discriminative representation learning. To address this issue, we propose DGR-MAE, a teacher–student masked image modeling framework based on posterior semantic correction for robust representation learning under incomplete observations. Unlike existing semantic-guided masking methods that modify token visibility during input construction, DGR-MAE preserves high-ratio stochastic masking in the student branch and introduces semantic correction after visibility degradation through teacher-guided differential reconstruction. Specifically, a semantic-aware teacher branch estimates patch-level importance to partition masked regions into semantic-critical and non-critical subsets, enabling region-dependent reconstruction prioritization. A collaborative feature refinement mechanism is further incorporated to enhance contextual consistency and structural reasoning during pretraining. To support controlled evaluation, we construct the ASRAir benchmark with hierarchical cloud occlusion levels. Experimental results show that DGR-MAE achieves 74.28% Top-1 accuracy on ASRAir-Occ and achieves the best Top-1 performance while maintaining competitive Top-5 accuracy compared with representative self-supervised baselines. In particular, it demonstrates substantially improved robustness under moderate-to-severe cloud occlusion, validating the effectiveness of posterior semantic correction for remote sensing representation learning under visibility degradation. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111852 |
