Declouding of Satellite Images for Crop Growth Monitoring Via Unrolling of Gradient Graph Laplacian Regularizer
Bibliographic record
Abstract
Spectral images periodically captured by satellites are often obscured by clouds. For crop monitoring, cloud removal—called “declouding”—in satellite images is important, so that crop growth at a field level can be estimated from restored images. In this paper, we adopt a graph signal processing (GSP) approach to satellite image declouding to capture neighboring pixel correlations that persist over time. We first assume an atmospherical scattering model (ASM) for image formation, where observation $\mathbf{y}$ is a product of piecewise constant (PWC) target image x and piecewise planar (PWP) transmission map t. To decompose y back into $\mathbf{x}$ and $\mathbf{t}$, we formulate respective quadratic programming (QP) problems, without / with box constraints, using — graph Laplacian regularizer (GLR) / gradient graph Laplacian regularizer (GGLR) as prior, to compute $\mathbf{x} / \mathbf{t}$ alternately. For efficient optimization, we compute $\mathbf{x}$ in an unconstrained QP via conjugate gradient (CG) without matrix inverse, while we compute t in a constrained QP via proximal gradient descent (PGD). We unroll iterations of our alternating algorithm into neural layers for end-to-end data-driven parameter optimization, resulting in an interpretable, algorithm-specific feed-forward network. Experimental results show that our unrolled network outperforms model-based and pure deeplearning schemes in declouded image quality, objectively and subjectively.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".