An Iterative Method for Hyperspectral Pixel Unmixing Leveraging Latent Dirichlet Variational Autoencoder
Bibliographic record
Abstract
We develop a hyperspectral pixel unmixing method that uses a Latent Variational Autoencoder within an analysis-synthesis loop to (1) construct pure spectra of the materials present in an image and (2) infer the mixing ratios of these materials in hyperspectral pixels without the need of labelled data. On OnTech-HIS-Syn-6em synthetic dataset that contains pixel unmixing groundtruth, the proposed method achieves acc = 100%, SAD = 0.0582 and RMSE = 0.0695 for segmentation, endmember extraction and abundance estimation, respectively. On HYDICE Urban benchmark, the proposed method achieves acc = 72.4%, SAD = 0.1669 and RMSE = 0.1984 for segmentation, endmember extraction and abundance estimation, respectively. Additionally, we applied this technique for crop analysis on hyperspectral data collected by the United States Department of Agriculture and achieved a coefficient of determination R2= 0.7 with respect to the ground truth. These results confirm that the proposed method is able to perform pixel unmixing without using labelled data.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".