CLDiff: Weakly Supervised Cloud Detection With Denoising Diffusion Probabilistic Models
Bibliographic record
Abstract
Cloud detection is an essential step in remote sensing (RS) image processing, contributing to various applications. However, existing fully supervised cloud detection methods rely on massive pixel-wise annotations, which are expensive and time-consuming. To alleviate the annotation burden, weakly supervised cloud detection (WSCD) has received extensive attention recently. One standard approach performs cloud detection within a classification paradigm, which inevitably faces category ambiguity when detecting semitransparent clouds. To tackle this problem, we propose a novel WSCD framework based on the diffusion model, termed CLDiff. Specifically, a multiscale feature rectification (MFR) module is introduced to extract multiscale semantic features in the encoder, enabling a definite identification of clouds and mitigating interference from bright objects in the background. Considering that clouds exhibit varying optical thicknesses, a diffusion decoder is developed to model the intraclass variations of clouds in a generative strategy, improving thin cloud detection. Initially, it devises a Gaussian modulation function to recalibrate ambiguous cloud activations and emphasize semitransparent clouds. Subsequently, these modulated activations serve as semantic guidance to optimize the diffusion process. This approach enables CLDiff to activate cloud contours under definite semantic conditions and avoids the additional branches for semantic learning as found in previous methods. Experimental results demonstrate that CLDiff achieves state-of-the-art performance in WSCD. A public reference implementation of this work in PyTorch is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/YLiu-creator/CLDiff</uri>.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".