Pansharpening for Incompletely Overlapping Image-pairs via Dictionary Extension
Bibliographic record
Abstract
There are currently various pansharpening methods to reconstruct a high-spatial-resolution multispectral image (HR-MSI) by fusing a low-spatial-resolution multispectral image (LR-MSI) with a high-spatial-resolution panchromatic image (HR-PAN).However, these methods can only handle situations where HR-MSI and LR-PAN cover the exactly same area, but in real practical situations, HR-PAN usually covers a larger area than the LR-MSI.As a result, these methods can only be used within the overlapping area and cannot reconstruct HR-MSI in the non-overlapping area.To solve this problem, we propose a pansharpening method for incompletely overlapping HR-PAN and LR-MSI image-pairs based on dictionary extension (termed PANDE), which can extend the dictionary learned in the overlapping area to the non-overlapping area, and reconstruct the entire HR-MSI on the entire area covered by the HR-PAN, in the framework of sparse expression.Specifically, in the overlapping area, the fusion model based on decomposition incorporating with the constraints of low-rank and sparsity is used to acquire the spectral dictionary and its associated coefficients matrix.Then, the spectral dictionary learned with the overlapping area will be extended to the non-overlapping area, under the guarantee of spectral similarity between adjacent areas, and its corresponding coefficients matrix will be obtained only using the HR-MSI with a sparse constraint.Finally, the desired HR-MSI can be reconstructed by using the spectral dictionary, the coefficients matrix of the overlapping area and that of the non-overlapping area.Experimental results on different scenes show that, compared with the other related methods, our proposed PANDE achieves a better fusion effect and can solve the problem of their inability to reconstruct HR-MSI in the non-overlapping areas.
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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 | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".