A fast and precise registration method for repeat-pass interferometric ALOS PALSAR data through baseline estimation
Bibliographic record
Abstract
The precise co-registration is a premise in repeat-pass interferometric SAR (In-SAR) applications. To obtain a sub-pixel registration, the correlation coefficient of image patches is usually used to find the tie points in the re-sampled images. This procedure is computationally-expensive with a lot of re-sampling operations. Thus, an automatic gross registration is needed to reduce the times of re-sampling operations before the fine registration. In order to geometrically register the reference and repeat-pass data, the records of the satellite orbit and imaging geometry can be used. However, when this method is applied to repeat-pass ALOS PALSAR data, there is an offset of 20–30 pixels in the azimuth direction. As the interference baseline needs to be established before In-SAR information is derived, it is found that the positions of the satellite can be used to register the reference and repeat-pass data in the azimuth direction with less than one pixel offset. In this paper, this method was applied to a pair of ALOS PALSAR images acquired in northern Toronto, Ontario, Canada, on October 8th and November 23rd in 2008. The experimental result shows that the proposed method can obviously reduce the amount of the re-sampling operations and decrease the cost of computation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.002 |
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".