Rotation‐Invariant Self‐Similarity Descriptor for Multi‐Temporal Remote Sensing Image Registration
Bibliographic record
Abstract
Abstract In this paper, a novel approach for the registration of multi‐sensor remote sensing images with substantial time differences is proposed. The proposed method consists of four main steps. First, robust image features are extracted using the well‐known UR‐SURF (uniform robust‐speeded up robust features) algorithm. Second, the feature descriptors are generated using a novel method based on self‐similarity measure, named RISS (rotation invariant self‐similarity). The RISS descriptor is an inherent rotation‐invariant descriptor based on the gradient orientation histogram of correlation values and is very resistant against illumination differences. Third, the outlier rejection process is performed based on a simple improvement of graph transform matching, named LWGTM (localized weighted graph transformation matching). Finally, the estimation of the transformation model and the rectification process are done using TPS (thin‐plate spline) model and the bilinear interpolation method. Five multi‐sensor remote sensing image pairs with relatively long years of time difference are used for evaluation. The results indicate the capability of the proposed method for reliable remote sensing image registration. The average recall, precision, the number of extracted matched points and the average registration accuracy of the proposed method are about 31.6, 39.5, 4940, and 1.8 pixels, respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".