Optimizing Relative Radiometric Normalization: Minimizing Residual Distortions in Multispectral Bitemporal Images Using Trust-Region Reflective and Laplacian Pyramid Fusion
Bibliographic record
Abstract
Accurate relative radiometric normalization (RRN) is important for reliable multitemporal remote sensing image analysis. Traditional methods often depend on coregistered image pairs, limiting their applicability with unregistered data. Keypoint-based RRN (KRRN) relaxes this constraint but remains affected by residual radiometric errors due to normalization inaccuracies and nonlinear effects. This letter introduces a refinement strategy that leverages the Trust-Region Reflective (TRR) algorithm to optimize normalization parameters, coupled with Laplacian Pyramid (LP) fusion for seamless image integration. Evaluation on four multispectral image pairs from different sensors (e.g., Landsat 8 and Sentinel-2, IRS and Landsat 5, Landsat 7 and SPOT-5, and UK-DMC2 and Landsat 5) and one pair from the same sensor (Sentinel-2) showed that our method reduces residual radiometric discrepancies, achieving up to 29% lower RMSE than some well-known models. The source code and datasets are available on GitHub: https://github.com/ArminMoghimi/Tensor-based-keypoint-detection.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| 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 | 0.002 |
| 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".