Improved Pansharpening with Un-Mixing of Mixed MS Sub-Pixels near Boundaries between Vegetation and Non-Vegetation Objects
Bibliographic record
Abstract
Pansharpening is an important technique that produces high spatial resolution multispectral (MS) images by fusing low spatial resolution MS images and high spatial resolution panchromatic (PAN) images of the same area. Although numerous successful image fusion algorithms have been proposed in the last few decades to reduce the spectral distortions in fused images, few of these take into account the spectral distortions caused by mixed MS sub-pixels (MSPs). Typically, the fused versions of MSPs remain mixed, although some of the MSPs correspond to pure PAN pixels. Due to the significant spectral differences between vegetation and non-vegetation (VNV) objects, the fused versions of MSPs near VNV boundaries cause blurred VNV boundaries and significant spectral distortions in the fused images. In order to reduce the spectral distortions, an improved version of the haze- and ratio-based fusion method is proposed to realize the spectral un-mixing of MSPs near VNV boundaries. In this method, the MSPs near VNV boundaries are identified first. The identified MSPs are then defined as either pure vegetation or non-vegetation pixels according to the categories of the corresponding PAN pixels. Experiments on WorldView-2 and IKONOS images of urban areas using the proposed method yielded fused images with significantly clearer VNV boundaries and smaller spectral distortions than several other currently-used image fusion methods.
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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 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".