A context-driven pansharpening method using superpixel based texture analysis
Bibliographic record
Abstract
In this paper, we propose a context-driven injection scheme for pansharpening, in which the injection coefficients are computed over superpixel segments obtained by means of a modified Simple Linear Iterative Clustering (t-SLIC) technique applied on the texture descriptors of the PAN image. By using the t-SLIC algorithm, various homogeneous-connected components can be generated according to their spectral properties. The proposed pansharpening method relies on a multiresolution framework by employing the Generalized Laplacian Pyramid (GLP) tailored to the Modulation Transfer Function (MTF) of the MS sensors for extracting the high frequency details. First, the injection gains are locally computed as regression coefficients between the upsampled MS and low-resolution PAN regions at a reduced scale. Then, they are multiplied by a global weighting factor computed per spectral band and defined as the ratio of variance between expanded MS bands and PAN image. Finally, the spatial details are modulated by means of the estimated global-local injection coefficients at superpixel level to produce the high-resolution MS image. The validation is assessed with two datasets acquired by IKONOS and WorldView-3 satellites. The experimental results show that the proposed method achieves a favourable performance both visually and quantitatively compared to the state of-the-art pansharpening algorithms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".