Hierarchical Band Selection Using the N-Dimensional Solid Spectral Angle Method to Address Inter- and Intra- Class Spectral Variability
Bibliographic record
Abstract
The contiguous narrow bands in hyperspectral data can hamper the accurate discrimination of targets especially for spectrally similar materials. The N-dimensional Solid Spectral Angle (NSSA) is a novel method that selects important bands for the maximum spectral separation of materials. This paper proposed a strategy of hierarchical band selection based on the NSSA method to address inter-and intra-class variability among materials. Bands are separately selected from different hierarchies of categorized materials using NSSA and the individual band sets are then combined. To evaluate this Hierarchical Band Selection with NSSA (HBS-NSSA), two hyperspectral datasets were analyzed that include airborne image endmembers for geological mapping and leaf spectra for tree species discrimination. Selected bands agree well with known features identified from expert knowledge. The results suggest that the HBS-NSSA method is both practical and effective and could be easily adopted in any other fields of application with spectrally similar materials.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".