Calculation of abundance factors in hyperspectral imaging using genetic algorithm
Bibliographic record
Abstract
Spatial resolution is a limiting factor in satellite imaging systems. It is usually very difficult to successfully interpret objects from a coarse resolution image. Images at such coarse resolutions result in mixed pixels. Mixed-pixel decomposition or spectral unmixing applies to derivation of constituent components, endmembers(EM), and their fractional proportions(abundances) at the subpixel scale. The mathematical intractability of the abundance non-negative constraint results in complex and extensive numerical approaches. Due to such mathematical intractability, many least square error(LSE) based methods are unconstrained and can only produce sub-optimal solutions. In this paper we propose a mixed genetic algorithm and LSE-based EM estimation method (LSEM) to extract the EM matrix and related abundances vectors. We apply the proposed GA-LSEM method to the subject of unmixing hyperspectral data. The experimental results obtained from simulated images show the effectiveness of the proposed method, specifically the robustness to noise.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".