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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".