Polarimetric active imaging in dense fog
Bibliographic record
Abstract
Operation under degraded visual environment (DVE) presents important strategic advantages. 3D mapping has been performed under DVE and good quality images have been obtained through DVE with active imaging systems. In these applications, the presence of fog clouds degrades the quality of the remotely sensed signal or even renders the operation totally impossible. In view of making the active imaging method more robust against dense fog, the use of polarimetry is herein studied. Spherical particles typical of fog do not depolarize incident polarized light in the backscattering (180°) direction. So, in principle, there should be less dazzling caused by aerosols for active imaging systems operating using the secondary polarization. However, strong depolarization still occurs at angles close to 180°. The greater the ratio of size to wavelength, the closer to 180° will the depolarization occur. When the cloud optical depth is small, the major scattering events seen by an active camera are the single backscattering events. However, when the optical depth of the cloud is higher than 1, multiple scattering becomes more important and causes depolarization due to the backscattering around 180°. The physics of this process will be discussed. Experimental results supporting the analysis will be presented. Those experimental results were obtained under controlled environment using the DRDC-Valcartier aerosol chamber. The experimental method herein proposed is based upon the use of ICCD range gated cameras wherein gate width and gate location may be varied on the fly. The optimal conditions for the use of these devices in view of obtaining the best image contrast are experimentally studied and reported in this paper.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".