Spectral–Spatial Semisupervised Hyperspectral Classification Using Adaptive Neighborhood
Bibliographic record
Abstract
Semisupervised learning (SSL) methods have shown great potential in the hyperspectral classification with a limited number of labeled samples. In this paper, we suggest a new graph-based SSL (GBSSL) using both spectral and spatial information. In the first step, we constructed two graphs using spectral and spatial information. Then, the Laplacians of both spectral and spatial graphs were merged to form a weighted joint graph. To improve the quality of spatial neighborhood for conforming the image objects, we employed the adaptive neighborhood (AN) technique. Instead of using the conventional crisp spatial neighborhood, the flat zone area filtering approach was used to define AN and extract the spatial information. By this way, each pixel is only connected to the pixels of a single flat zone, which presents a particular object in the image. Consequently, the border between different classes is extracted more precisely. As a result, the final classified map is more homogenous, and the salt and pepper effect is removed. To evaluate the efficiency of the proposed method, the experiments were carried out on three real benchmark hyperspectral data sets with different types of land cover, and different spectral and spatial resolutions. The results of the proposed method showed excellent performances in all data sets, specifically where a very limited number of labeled training samples were available. This method achieves a significant improvement compared to the state-of-the-art classifiers such as SVM, spectral-spatial SVM, and spectral-spatial GBSSL.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".