<title>Effect of anomalies on data compression onboard a hyperspectral satellite</title>
Why this work is in the frame
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Bibliographic record
Abstract
The Canadian Space Agency (CSA) is developing a pre-operational spaceborne Hyperspectral Environment and Resource Observer (HERO). HERO will be a Canadian optical Earth observation mission that will address the stewardship of natural resources for sustainable development within Canada and globally. To deal with the challenge of extremely high data rate and the huge data volume generated onboard, CSA has developed two near lossless data compression techniques for use onboard a satellite. CSA is planning to place a data compressor onboard HERO using these techniques to reduce the requirement for onboard storage and to better match the available downlink capacity. Anomalies in the raw hyperspectral data can be caused by detector and instrument defects. This work focuses on anomalies that are caused by dead detector elements, frozen detector elements, overresponsive detector elements and saturation. This paper addresses the effect of these anomalies in raw hyperspectral imagery on data compression. The outcome of this work will help to decide whether or not an onboard data preprocessing to remove these anomalies is required before compression. Hyperspectral datacubes acquired using two hyperspectral sensors were tested. Statistical measures were used to evaluate the data compression performance with or without removing the anomalies. The effect of anomalies on compressed data was also evaluated using a remote sensing application.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it