Comparison of deep and shallow one-class classifiers for detecting invasive Prosopis trees in Kenya from airborne hyperspectral data
Bibliographic record
Abstract
• ITreeDet (2D CNN) exhibited the best performance among several one-class classifiers (F1 = 0.81). • The key wavelength regions were blue and green, and to some degree red and red-edge. • The results emphasise the importance of classifier selection in invasive species mapping. Prosopis spp. are globally widespread woody invasive plants that negatively impact biodiversity and rural livelihoods. Accurate distribution information is essential for understanding their ecological consequences and for effective conservation and land management. In this study, we evaluated the suitability of airborne hyperspectral data in the visible and near-infrared (VNIR) range for pixel-level mapping of Prosopis trees at the beginning of the dry season in Kenya. For the mapping, we compared the performance of five one-class classifiers, a type of weakly supervised method trained solely using labels for the target class. The classifiers compared were Biased Support Vector Machine (BSVM), Maximum Entropy (Maxent), Boosted Regression Tree (BRT), ITreeDet (with experiments using both 2D and 3D convolutional neural networks [CNNs]), and HOneCls (a 2D fully convolutional neural network). The models were trained using observations of 120 Prosopis trees across the study area, corresponding to 2561 pixels, along with 10,000 unlabeled tree pixels. Testing was done using data from five 1 ha study plots, totalling 14,637 tree pixels. To analyse feature importance we used a model-agnostic permutation approach. F1-score was highest for ITreeDet with 2D CNN (0.81), followed by MaxEnt (0.73), HOneCls (0.71), BSVM (0.68), and BRT (0.65). The critical hyperspectral features were consistent across the models and primarily in the blue and green region, with some importance in the red and red-edge region, while near-infrared showed low importance. The results highlight the effectiveness of VNIR hyperspectral data and one-class classification for mapping Prosopis trees in Kenya. The demonstrated methodology is a promising tool for high-resolution mapping of Prosopis trees, supporting ecosystem restoration and conservation efforts.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".