Maxent: An Optimal Neighbor Selection for Multispectral Airborne Lidar Point Cloud Classification
Bibliographic record
Abstract
Multispectral Airborne Light Detection And Ranging (LiDAR) technology is recently invented to improve the capability of classification through incorporating fruitful spectral information. Some existing methods construct contextual features of point clouds by querying a fixed scale of neighbor points or selecting an optimal neighborhood. However, they may introduce heterogeneity from the local neighborhood or select insufficient neighbor points, hindering the performance of classification. Therefore, we propose an optimal neighbor selection method based on the maximum entropy (MaxEnt) principle to determine the homogeneous neighborhood of each point. The contextual features extracted from selected optimal neighborhood are then served as input into various machine learning classifiers for point cloud classification. Extensive experiments are conducted to compare the performance of MaxEnt against six other neighbor selection methods, which demonstrate that MaxEnt can achieve better classification results on multispectral airborne LiDAR data collected by Optech Titan in terms of overall accuracy improvement by 7.3-19.1%.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".