A Maximum Entropy-Based Optimal Neighbor Selection for Multispectral Airborne LiDAR Point Cloud Classification
Bibliographic record
Abstract
Multispectral LiDAR technology was recently invented to improve the capability of thematic mapping through incorporating visible/infrared spectral information. Similar to image processing, point cloud classification usually considers contextual features derived from surrounding points to improve the model accuracy. Some of the existing methods construct contextual features of point clouds by querying a fixed scale/number of neighbor points or selecting a variable size neighborhood based on some optimality criterion. Although these methods are able to collect neighbor points to derive contextual features, they may also in turn 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. More specifically, the proposed method determines the homogeneity of local neighborhood of each point and constructs geometric and radiometric features based on the use of MaxEnt to determine optimal points nearby. The constructed contextual features 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. The experimental results demonstrate that MaxEnt is able to achieve better classification results on multispectral airborne LiDAR data collected by Optech Titan in terms of overall accuracy improvement by 7.3-19.1%. Moreover, MaxEnt is proven to be more suitable for land cover scenarios with imbalanced classes caused by detailed and tiny objects, e.g., perimeter fencings and power lines, than other existing neighbor selection methods.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".