CORRECTION OF OVERLAPPING MULTISPECTRAL LIDAR INTENSITY DATA:POLYNOMIAL APPROXIMATION OF RANGE AND ANGLE EFFECTS
Bibliographic record
Abstract
Abstract. Recent development of radiometric calibration, correction and normalization approaches have facilitated the use of monochromatic LiDAR intensity and waveform data for land surface analysis and classification. Despite the recent successful attempts, the majority of existing approaches are mainly tailor made for monochromatic LiDAR toward specific land surface scenario. In view of the latest development of multispectral LiDAR sensor, such as the Optech Titan manufactured by Teledyne Optech, a more generic approach should be developed so that the radiometric correction model is able to handle and compensate the laser energy loss with respect to different wavelengths. In this study, we propose a semi-physical approach that aims to utilize high order polynomial functions to model the distortion effects due to the range and the angle. To estimate the parameters of the respect polynomial functions for the range and angle, our approach first locates a pair of closest points within the overlapping LiDAR data strips and subsequently uses a non-linear least squares adjustment to retrieve the polynomial parameters based on the Levenberg-Marquardt algorithm. The approach was tested on a multispectral airborne LiDAR dataset collected by the Optech Titan for the Petawawa Research Forest located in Ontario, Canada. The experimental results demonstrated that the coefficient of variation of the intensity of channel 1 (1550 nm), channel 2 (1064 nm) and channel 3 (532 nm) were reduced by 0.1 % to 39 %, 10 % to 45 % and 12 % to 54 %, respectively. The striping noises, no matter found within single strip and overlapping strips, were significantly reduced after implementing the proposed radiometric correction.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".