ISCE Docker Tools: Automated Radiometric Terrain Correction and Image Coregistration of Uavsar MLC Data
Bibliographic record
Abstract
In Summer/Fall 2019, the UAVSAR platform was used to collect dense time series over several agricultural and biomass sites in the southeastern US. This data is to be used for developing ecosystem science algorithms for the upcoming NASA-ISRO SAR (NISAR) mission. Development and testing of these algorithms require routine SAR processing steps such as image co-registration and terrain correction. Because the NISAR mission will use the Interferometric Synthetic Aperture Radar (InSAR) Scientific Computing Environment (ISCE) for data processing from Level 0 through Level 2, we focused on developing a new ISCE workflow to facilitate UAVSAR Multi-Looked-Cross Products (MLC) data processing. The workflows are python scripts and can be readily modified according to user needs. They operate in conjunction with a Docker image of ISCE, which allows data processing on any system that supports Docker (https://www.docker.com/). A defining feature of this workflow is that it usually only requires minimal interaction by the user: the user only needs to provide the desired UAVSAR MLC data and run one docker command to initiate the data processing.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.024 |
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".