Unsupervised Bayesian Subpixel Mapping Autoencoder Network for Hyperspectral Images
Why this work is in the frame
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Bibliographic record
Abstract
Unsupervised subpixel mapping (SPM) of hyperspectral image (HSI) is a challenging task due to the difficulties to integrate different prior information and model constraints into a coherent framework. This paper presents a Bayesian neural network for unsupervised HSI SPM, which has the following characteristics. First, the deep image prior (DIP) achieved by a fully convolutional neural network (FCNN) is used to model the spatial correlation efficiently and adaptively in the subpixel label domain. Second, a discrete spectral mixture model (DSMM) is designed to leverage the forward model for enhanced SPM. Third, an auto-encoder architecture is designed to integrate the FCNN and the DSMM to allow efficient unsupervised representational learning using both data and knowledge. Fourth, an expectation-maximization approach is designed to solve the resulting maximum a posteriori problem, where a purified means approach extracts endmembers, and the gradient descent approach updates FCNN parameters for subpixel label estimation. Comparative experiments on both real and simulated HSIs demonstrate that the proposed method outperforms other state-of-the-art methods in terms of both numerical accuracies and visual subpixel mapping results.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it