Active Learning-Based Spectral–Spatial Classification for Discriminating Tree Species in Hyperspectral Images
Bibliographic record
Abstract
Exploiting spectral–spatial information and reducing the number of required training samples are important for improving tree species classification performance in hyperspectral images. In this article, an active learning-based spectral–spatial classification (ALSSC) model is proposed to reduce the demand for training samples while improving the classification performance. To improve classification performance, the proposed ALSSC employs two ways to exploit spectral–spatial information within the hyperspectral image: 1) features used in classification are extracted from multiscale superpixels; 2) the classification result is refined by guided filtering and subsequently employed as the input for the next round of classification. To reduce the demand for training samples, after each round of classification, active learning (AL) is adopted to select the most informative samples from the unlabeled testing set to enrich the training set. To validate the effectiveness of the proposed ALSSC, experiments are conducted using a tree species classification dataset collected by an airborne hyperspectral sensor. Remarkably, when compared to the state-of-the-art AL-based approach using the same number of labeled samples, the ALSSC demonstrates an accuracy improvement of 11.62%. In addition, trained with fewer labeled samples, the ALSSC outperforms state-of-the-art spectral–spatial classification methods that do not incorporate AL.
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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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".