Label-Decoupled Style Augmentation for Domain Generalization in Multi-Label Remote Sensing Scene Classification

• URL: http://arxiv.org/abs/2607.12704v3
• Date: Mon, 20 Jul 2026 12:34:22 GMT
• Title: Label-Decoupled Style Augmentation for Domain Generalization in Multi-Label Remote Sensing Scene Classification
• Abstract summary: Multi-label classification assigns several co-occurring labels to each aerial scene, yet deployed models often encounter data distributions different from their training.<n> Feature-statistics augmentation such as MixStyle, EFDMix, and correlated style uncertainty improves generalization at low cost but perturbs channel statistics globally.<n>A label-decoupled augmentation framework is proposed, confining style perturbation to label-specific regions.
• Score: 4.411658619208916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-label classification assigns several co-occurring labels to each aerial scene, yet deployed models often encounter data distributions different from their training. Feature-statistics augmentation such as MixStyle, EFDMix, and correlated style uncertainty improves generalization at low cost but perturbs channel statistics globally, treating each image as a single style; one class can then contaminate the augmentation of another. Domain generalization is understudied for multi-label remote sensing; no prior method or multi-source benchmark targets it. A label-decoupled augmentation framework is therefore proposed, confining style perturbation to label-specific regions. Per-label attention, obtained from a learnable module or from gradient class-activation maps, yields per-label feature statistics; these statistics are mixed with cross-domain samples that share present labels, under independent per-label coefficients, and features are recomposed by attention-weighted normalization. Three operators combined with two attention sources produce six variants, evaluated on a leave-one-domain-out benchmark from multi-label UCM, AID, and DFC15 over six shared labels. Averaged over three splits and five seeds, the best variant attains 71.5% mean average precision, exceeding empirical risk minimization by 5.0 points and the strongest global-statistics baseline by 1.3 points, with the largest gain on the hardest transfer (up to 7.7 points). Ablations indicate that spatial attention and refreshed localization maps are most influential. The framework adds at most 0.35% parameters, leaves inference unchanged, and appears to offer a generic, inexpensive upgrade path for multi-label statistics-based domain generalization. Code is available upon acceptance at https://github.com/Alaa-Almouradi/Style-Augmentation-Upgrade.

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