Spatial-Spectral Random Patches Network for Classification of Hyperspectral Images
Bibliographic record
Abstract
Random patches network (RPNet) is an emerging deep learning method that can effectively extract the deep features from hyperspectral images.However, this network only relies on spectral bands in feature extraction, failing to make use of the information-rich spatial features.This paper puts forward another variant of the RPNet, named G-RPNet.The proposed network extracts the deep hierarchical Gabor features, with Gabor spatial features as inputs.The extracted deep hierarchical features were stacked to those extracted by the RPNet, and the final feature vectors were classified by the support vector machine (SVM).The integrated feature vectors inherit the merits of the deep hierarchical features of both RPNet and G-RPNet, laying a solid basis for the classification of hyperspectral images.Experiments were conducted on two real hyperspectral images (Indian Pines and Pavia University) from agricultural and urban areas.The results prove the superiority of the proposed method in the classification of hyperspectral images over some recent shallow and deep spatial-spectral classification techniques.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".