Tree Species Classification on Hyperspectral Imagery Using Fewer Training Samples
Bibliographic record
Abstract
Abstract. The distribution of tree species within the forest holds significant importance for forest management. Since field surveys in the forest are time-consuming and cost-expensive, automatically extracting tree species distribution maps from remote sensing imagery becomes a trend. For tree species classification using hyperspectral imagery, many existing classification methods require a large number of training samples to achieve high classification accuracy. However, the classification accuracy will decrease rapidly if only a few hundred training samples are used. Given the challenges and expenses associated with collecting abundant training samples in the forest, there is a need to explore methods that achieve good classification performance with a limited number of training samples. In this paper, a classification scheme combining SuperPCA and Active Learning (AL) is proposed to improve the tree species classification using a limited number of training samples. SuperPCA is employed to reduce feature dimensions and harness spectral-spatial information within hyperspectral imagery. Active Learning is employed to select informative samples for the training, thus reducing the requirement for training samples. Experiments on a tree species classification data set demonstrate the effectiveness of the proposed classification scheme.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".