A Maximum Entropy Based Outlier Removal for Airborne LiDAR Point Clouds
Bibliographic record
Abstract
Airborne light detection and ranging (LiDAR) data often suffer from noisy returns hovering in empty space within the collected 3-D point clouds. This can be attributed to system-induced factors, such as timing jitter and range walk error, or instantaneous air conditions, such as smoke, rain, clouds, etc. These floating points are indeed outliers, which significantly affect the subsequent analytical processes. Though various point cloud denoising methods are proposed based on sparsity assumption and elevation, they are highly unlikely to remove both clustered and scattered noisy points, especially those located close to the point clouds. Meanwhile, the performance of existing methods does not perform well when noisy points appear close to the ground or on rugged terrain. Accordingly, we propose a maximum entropy based outlier removal (MEOR) method for airborne LiDAR point clouds. More specifically, the proposed method includes two stages, i.e., one global coarse outlier removal stage (MEOR-G) and the subsequent local refined outlier removal stage (MEOR-L). In each stage, the MEOR algorithm is exploited to 1) produce an elevation histogram for the point clouds, 2) search for the elevation threshold to distinguish noisy points and valid points, and 3) remove noisy points and preserve valid data points. We conduct several comprehensive experiments to compare the performance of our proposed MEOR against four other existing noisy point removal methods on four LiDAR datasets. The experimental results demonstrate that MEOR significantly outperforms four other denoising methods by simultaneously removing clustered and scattered noisy points and achieves an improvement by 0.126–99.815%, 0–100%, 0.001–8.454%, and 0.053–99.691% in terms of recall, precision, overall accuracy, and F1 score, respectively.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".