Current status of satellite data compression in Canadian Space Agency
Bibliographic record
Abstract
One of the challenges in the development of a hyperspectral satellite is the extremely high data rate due to the huge data volume generated on board, which exceeds the downlink capacity, and may quickly exhaust the onboard storage capacity. To deal with this challenge the Canadian Space Agency (CSA) has been developing data compression technologies for satellite imagery data for many years. Compression techniques for operational use have been developed. Recently, two near lossless data compression techniques for hyperspectral imagery have been developed and implemented in hardware. The CSA is considering a near lossless data compressor for use on-board a hyperspectral satellite in order to reduce the requirement for on-board storage and to better match the available downlink capacity. This invited paper is to review the research and development of satellite data compression for hyperspectral imager at CSA, briefly summarize the two near lossless compression techniques, to address the application based assessment of the impact of lossy or near lossless data compression on Earth observation applications, and to provide up-to-date status of the hardware implementation of the on-board data compression technologies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".