Implementation and Evaluation of Concurrent Gradient Search Method for Reprojection of MODIS Level 1B Imagery
Bibliographic record
Abstract
This paper presents details regarding implementation of a novel algorithm for reprojection of Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1B imagery. The method is based on a simultaneous 2-D search in latitude and longitude geolocation fields by using their local gradients. Due to the segmented structure of MODIS imagery caused by the instrument whiskbroom electrooptical design, the gradient search is realized in the following two steps: intersegment and intrasegment search. This approach resolves the discontinuity of the latitude/longitude geolocation fields caused by overlap between consecutively scanned MODIS multidetector image segments. The structure of the algorithm allows equal efficiency with nearest neighbor and bilinear interpolation. A special procedure that combines analytical and numerical schemes is designed for reprojecting imagery near the polar region, where the standard gradient search may become unstable. The performance of the method was validated by comparison of reprojected MODIS/Terra and MODIS/Aqua images with georectified Landsat-7 Enhanced Thematic Mapper Plus imagery over Canada. It was found that the proposed method preserves the absolute geolocation accuracy of MODIS pixels determined by the MODIS geolocation team. The method was implemented to reproject MODIS Level 1B imagery over Canada, North America, and Arctic circumpolar zone in the following four popular geographic projections: Plate Care (cylindrical equidistant), Lambert Conic Conformal, Universal Transverse Mercator, and Lambert Azimuthal Equal-Area. It was also found to be efficient for reprojection of Advanced Very High Resolution Radiometer and Medium Resolution Imaging Spectrometer satellite images and general-type meteorological fields, such as the North American Regional Reanalysis data sets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".