A New Endmember Extraction Method Based on Least Squares
Why this work is in the frame
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Bibliographic record
Abstract
Endmember extraction is frequently adopted to detect spectrally unique signatures of pure ground materials in hyperspectral imagery. These endmembers are the purest pixels in the HSI data cubes. Every pixel in a HSI data cube can be expressed as a linear combination of a finite number of endmembers. In this paper, we propose a novel method for endmember extraction by means of least squares. We perform minimum noise fraction to reduce the dimensionality of the data cube, initialize the endmembers by using automatic target generation process, compute the abundance map from the dimensionality reduced data cube and the initial endmembers, and calculate the final endmembers by using least squares. Our proposed method is comparable to and sometimes outperforms existing methods in term of spectral angle distance for all four testing data cubes for endmember extraction. In addition, our method is relatively fast as well because it only performs quite simple operations to find endmembers in the testing hyperspectral data cubes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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