Remote sensing provides vast unlabeled imagery at low cost, yet annotation remains expensive, making self-supervised learning (SSL) well suited to this domain. However, existing DINO-style SSL frameworks are not well suited to remote sensing imagery, where object extents vary substantially and standard multi-crop view generation often introduces cross-scale inconsistency. This issue is particularly severe for small objects and elongated structures, whose discriminative features can be lost under scale transformations. To address this limitation, we propose DINO-MS (DINO with multi-scale consistency), a scale-consistent SSL framework for remote sensing imagery. The key idea is to construct feature-aligned cross-scale local views and explicitly enforce prediction-level agreement among them. Specifically, DINO-MS first adopts a co-located multi-scale cropping strategy to sample local views from the same spatial location at different crop scales, and then introduces a local consistency loss that works jointly with the original DINO local-to-global objective. Extensive experiments on land-use classification and change detection benchmarks show that DINO-MS generally improves downstream transfer performance. Notably, on EuroSAT, it improves per-class accuracy from 80.60% to 87.80% for Highway and from 88.00% to 91.60% for River with DINO-MC, confirming its advantage for categories dominated by small objects.
Zou et al. (2026) studied this question.