DDCI: Unsupervised Domain Adaptation for Remote Sensing Images Based on Diffusion Causal Distillation
Bibliographic record
Abstract
The distribution of remote sensing (RS) images can vary significantly due to seasonal changes and lighting conditions, making it difficult for deep learning models to generalize effectively across different RS datasets. This variation leads to a domain gap that hampers model performance when applied to new, unseen data. To tackle this challenge, we introduce DDCI, a novel unsupervised domain adaptation (UDA) framework designed to bridge the domain gap in RS image perception. Our framework consists of two key components, i.e., the adaptation diffusion distillation (ADD) module and the consistent causal intervention (CCI) module. The ADD module addresses the domain gap by aligning the source and target domains. It enhances the representation of the target domain by distilling semantic knowledge from the teacher model of the source domain. This process allows the target domain to benefit from the rich features of the source domain, leading to improved model generalization. The CCI module focuses on removing spurious correlations between domain-agnostic knowledge and domain-specific knowledge. By carefully considering the distinct characteristics of the target domain while preserving the specificity of the source domain, the CCI module ensures that only relevant, causal information is transferred between domains. This prevents overfitting to irrelevant domain-specific features and enhances model robustness. We demonstrate the effectiveness of the DDCI framework on RS scene classification tasks, utilizing four widely recognized RS datasets. Our results show significant performance improvements, underscoring the potential of this approach to boost the adaptability of deep learning models across diverse RS image datasets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".