Extraction of Endmembers From Hyperspectral Images Using A Weighted Fuzzy Purified-Means Clustering Model
Bibliographic record
Abstract
Hyperspectral endmembers are the spectra of pure materials that are responsible for generating the mixed pixels in hyperspectral images (HSIs). Hyperspectral endmember extraction (HEE) is essentially an inverse problem, where the unknown endmembers are inferred from the spectral measurements. Efficient extraction of endmembers in HSI relies on a well-defined generative model that captures key factors in HSI generation process, such as the clustering effect in the spatial domain and the noise heterogeneity effect in the spectral domain. This paper presents a weighted fuzzy purified-means (WFP-means) clustering model for HEE, where the endmembers are modeled as mean vectors of individual classes, and the fractional contributions of individual endmembers, called abundances, are treated as soft class membership. Accordingly, an endmember is estimated as the weighted mean of purified pixels in HSI, while the abundances are estimated as the nonnegative regression coefficients. In contrast to a mixed pixel that consists of multiple endmembers, a “purified pixel” is due to a single endmember. The introduction of the concept of “purified pixels” into the fuzzy clustering model leads to an elegant optimization scheme. Moreover, the proposed model accounts for the noise variance heterogeneity issue, which is essential for achieving unbiased abundance estimation. The proposed method is tested on both simulated and real HSI, in comparison with several other HEE methods. The results demonstrate that the proposed method compares favorably with respect to the referenced methods in terms of both endmember and abundance estimation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".