Impacts of lossy compression on hyperspectral products for forestry
Bibliographic record
Abstract
Hyperspectral data from satellites are voluminous. Satellite data from a hyperspectral sensor can be transmitted at either Ka-band rates without compression or at X-band rates with lossy compression through links to ground stations. If lossy compression is used, there can be a 10:1 reduction in the amount of data to be transmitted. The Canadian Space Agency (CSA) has developed algorithms for lossy compression. For Canada's Hyperspectral satellite HERO (Hyperspectral Earth Resource Observer), consideration is being given to lossy compression of the data prior to data transmission. Experiments have been conducted with Hyperion and AVIRIS data to assess the impacts of compression on forest information products. These assessments have included forest classification products for forest inventory. This paper presents the results of these experiments with multiple analysis methods. The results indicate that 10:1 lossy compression produces too large a loss in information content in hyperspectral imagery for forest information products. Comparisons are given with other lossy compression methods
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".