Feature Extraction of Hyperspectral Images Based on Subspace Band Selection and Transform-Domain Recursive Filtering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During the feature extraction of hyperspectral images, a single filter cannot acquire complete information. To solve the problem, this paper proposes a feature extraction method based on subspace band selection and transform-domain recursive filtering. The proposed method contains three steps: Firstly, the target hyperspectral image is divided into multiple subsets of adjacent bands. Secondly, the Lasso-based band selection approach is adopted to compute the sparsity coefficient of each band. The bands in each subset are then ranked by the coefficient. Based on the ranking, the band with the highest coefficient is extracted from each subset, and used to reconstruct the hyperspectral data. Finally, the reconstructed hyperspectral image is processed through transform-domain recursive filtering, producing the features to be classified. Taking the support vector machine (SVM) as the classifier, our method was tested on several real hyperspectral image datasets. The results show that our method has a better classification accuracy than the other band selection methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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