Satellite image fusion with multi-scale wavelet analysis: Preserving Spatial Information and Minimizing Artifacts (PSIMA)
Bibliographic record
Abstract
Image fusion is the combination of two or more different images to form a new image that contains enhanced information. Consistent with specific application goals, a variety of image products arises from the many available fusion algorithms. However, there is no universal, quantitative performance measure to estimate image fusion quality. The essential objective of image fusion is that nearly all of the original application-specific information should be preserved, and artifacts should be minimized in the final product. The wavelet transform, a well-known and solid mathematical tool, has already been applied to multi-sensor image fusion. The wavelet transform allows the decomposition of an image into its constituent spatial scale layers. Most image fusion techniques, including wavelet analysis, require that the input images of different spatial resolutions and sample sizes first be re-sampled to achieve spatial registration. The re-sampling could cause a loss of spatial information or might introduce artifacts in the final fused image, especially when the resolutions of the input images are significantly different. In this paper, as a further development of the application of wavelet analysis to image fusion, we propose a new scheme for multi-resolution image fusion, Preserving Spatial Information and Minimizing Artifacts (PSIMA) with multi-scale wavelet analysis. With the PSIMA scheme, the images are fused in almost their original pixel size. Therefore, the finest spatial information of the input images can be preserved and artifacts minimized in the final fused product. We demonstrate the PSIMA scheme using RADARSAT-1 ScanSAR and NOAA AVHRR images. The results show that the PSIMA scheme is superior to conventional wavelet analysis for image fusion in terms of spatial information preservation and artifact rejection.
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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 | 0.002 |
| 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".