Approach based on SPEA2-band selection and Random Forest classifier to generate thematic maps from hyperspectral images.
Bibliographic record
Abstract
Hyperspectral images (HIs) and segmentation have become a promising solution for different applications such as the production of thematic maps (TMs) of agricultural areas. However, problems such as the Hughes phenomenon and high demand for computational resources are related to the high number of bands of HIs. This study proposes a hybrid approach of Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Random Forest classifier for producing TMs, aiming at band selection and improvement of average recall of segmentation. In experiments, the proposed approach reduced the number of bands on average from 220 to 30 in the Indian Pines image and from 224 to 42 in the Salinas image. The proposed approach was statistically whether identical or better than other approaches regarding the average recall of segmentation. Therefore, the proposed approach is promising as regards band selection and competitive in segmentation being a potential tool for generating TMs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".