RBF Neural Network for Landmine Detection in H Yperspectral Imaging
Why this work is in the frame
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Bibliographic record
Abstract
In this work, we evaluate different classification algorithms used for multi-target detection in hyperspectral imaging. We took into consideration the scenario of landmine detection in which we compared the performance of each method in various cases. In addition, we introduced the detection of targets using artificial intelligence-based methods in order to obtain better detection performance together with target identification and estimation of its abundance. These algorithms were tested on various types of hyperspectral images where the spectra of the landmines were planted in different proportions in the hyperspectral scenes. The results show the advantage of using our training strategy for radial basis function neural networks (RBFNN) in order to detect, identify and estimate the abundance of the targets in hyperspectral images at the same time. Moreover, the proposed technique requires a comparable computational cost with respect to state of art target detection techniques.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it