Enhancement of resilience to bit-errors of compressed data on-board a hyperspectral satellite using forward error correction
Bibliographic record
Abstract
To deal with the huge volume of data produced by hyperspectral sensors, the Canadian Space Agency (CSA) has developed two simple and fast algorithms for compressing hyperspectral data, namely Successive Approximation Multistage Vector Quantization (SAMVQ) and Hierarchical Self-Organizing Cluster Vector Quantization (HSOCVQ). The CSA intends to use these algorithms, which are capable of providing high compression rates, on-board a proposed Canadian hyperspectral satellite. It has been shown that both SAMVQ and HSOCVQ are near-lossless compression algorithms as their designs restrict compression errors to levels consistent with the level of the intrinsic noise in the original hyperspectral data. Although both of them are more bit-error resistant than the traditional compression algorithms, when the bit-error rate (BER) exceeds 10<sup>-6</sup>, the compression fidelity starts to drop apparently. This paper explores the benefits of employing forward error correction on top of data compression, by SAMVQ or HSOCVQ, to deal with higher BERs. In particular, it is shown that by proper use of convolutional codes, the resilience of compressed hyperspectral data against bit errors can be improved by close to two orders of magnitude.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".