Georeferencing of UK DMC stereo-images without ground control points by exploiting geometric distortions
Bibliographic record
Abstract
This article describes a new method for the georeferencing of UK-DMC imagery that does not require ground control points (GCPs). The proposed method utilizes satellite ancillary data, and the inter-imager offsets to determine the geolocation of individual pixels. The major step involved is the direct georeferencing of each pixel using satellite GPS and attitude sensor observations. The known separation between the sensors will allow us to determine the geolocations of all pixels that are taken at the same time using the same exterior orientation parameters. Traditional methods for georeferencing use GCPs, which are expensive and time-consuming tasks. Moreover, the traditional method is not suitable for a pushbroom imager because every scan line has a different set of exterior orientation parameters. Therefore, we propose a direct georeferencing approach without GCPs. The major source of error in direct georeferencing is the error in attitude measurements. The reason for this error is considered to be the thermo-elastic effects on the satellite, which affect the sensors’ positioning, causing deformation in the images. These effects have been modelled as a transformation matrix that describes the extent of deformation in the imagery, and is estimated by exploiting the geometric distortions in stereo Earth images. For this purpose, a mathematical model has been developed to demonstrate how inter-image offsets have been introduced into the imagery and affected by thermal deformation. The mathematical model is based on the sensor configuration of UK-DMC satellites. The model has been further inverted to extract the thermal deformation at a given row and column offset. The thermal deformation matrix has been found to mitigate the pointing error up to 1 km. The accuracy of the thermal deformation estimates is highly dependent on the accuracy of image offsets. The accuracy of image offsets is dependent on several factors, which include the image registration method, window size, along-track separation between the sensors, satellite attitude, and resolution of the sensors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".