Multiscale Occlusion-Robust Scene Classification in Remote Sensing Images via Supervised Contrastive Learning

Scene classification of remote sensing images plays a vital role in Earth observation applications. Among various challenges, occlusion is a prevalent and critical issue in practical applications, particularly when dealing with large-area occlusions caused by clouds, shadows, and man-made structures. Current methods, whether based on occlusion recovery or occlusion-robust feature extraction, generally show limited performance when processing extensive occluded regions due to ignoring the inconsistency in feature representation caused by multiscale occlusions. To address the occlusion challenge, this letter proposes a novel contrastive learning-based multiscale occlusion framework with three key components: 1) a pretext task module that distinguishes between small and large occlusions to enable occlusion-invariant feature learning; 2) a multibranch feature extraction network based on ResNet-50’s shared-weight convolutional layers for consistent feature extraction across occlusion levels; and 3) a joint loss function that adaptively balances contrastive feature learning and classification. Extensive experiments were evaluated on the DIOR-Occ and LEVIR-Occ benchmark datasets, demonstrating significant improvements in classification accuracy across different occlusion scenarios. Compared with existing approaches, the proposed framework achieves superior robustness and generalization capabilities, with notable advantages in the analysis of highly occluded data. Future research will explore the adaptation of this framework to detection and segmentation tasks.