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The research aims to improve fine-grained aircraft recognition in optical remote sensing images obscured by cloud occlusion.

June 6, 2026Remote SensingOpen Access

DGR-MAE: Posterior Semantic Correction Masked Autoencoder for Fine-Grained Aircraft Recognition Under Cloud Occlusion

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The research aims to improve fine-grained aircraft recognition in optical remote sensing images obscured by cloud occlusion.
Proposed DGR-MAE framework employs teacher-student masked image modeling.
Constructed the ASRAir benchmark with various cloud occlusion levels for evaluation.
Implemented a collaborative feature refinement mechanism for better contextual consistency.
Achieved 74.28% Top-1 accuracy on ASRAir-Occ, surpassing existing self-supervised methods.
Demonstrated improved robustness in recognizing aircraft under moderate-to-severe cloud occlusion, validating the use of posterior semantic correction.

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Randomized trial demonstrates improved aircraft recognition accuracy under cloud occlusion, highlighting effective representation learning methods.

Liu et al. (2026) studied this question.

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Randomized trial demonstrates improved aircraft recognition accuracy under cloud occlusion, highlighting effective representation learning methods.