Multitemporal image cloud removal using group sparsity and nonconvex low-rank approximation
Bibliographic record
Abstract
Remote sensing (RS) images are widely used in environmental monitoring, urban planning, and land surface classification.However, RS images are often polluted by cloud, which leads to the loss of some important information of RS images and hinders the development of relevant applications.The existing spatial-spectral total-variational regularization can only promote the spatial-spectral continuity of cloud component, but cannot maintain the shared group sparse mode of spatial difference images of different spectral bands.To solve this problem, we add the weighted ℓ 2,1 -norm to constrain the cloud component differential images, and use the nonconvex regularization term, namely the weighted nuclear norm, to replace the traditional nuclear norm, which solves the problem that the original nuclear norm violates the larger singular value.In summary, we propose a multitemporal image cloud removal model based on the weighted nuclear norm which is the nonconvex low-rank approximation and the group sparsity regularization (WNGS), where the group sparsity regularization and the weighted nuclear norm promote each other.The resulting problems are solved using the alternating direction method of multipliers.Numerical experiments both simulated and real multitemporal images demonstrate that the proposed method is superior to other advanced cloud-removal methods in different cloud-removal scenarios.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".