Underwater Multispectral Laser Serial Imager for Spectral Differentiation of Macroalgal and Coral Substrates
Bibliographic record
Abstract
The advancement of innovative underwater remote sensing detection and imaging methods, such as continuous wave laser line scan or pulsed laser (i.e., LiDAR—Light Detection and Ranging) imaging approaches can provide novel solutions for studying biological substrates and manmade objects/surfaces often encountered in underwater coastal environments. Such instruments can be used shipboard or coupled with proven and available deployment platforms as AUVs (Autonomous Underwater Vehicles). With the right planning, large areas can be surveyed, and more extreme and difficult-to-reach environments can be studied. A prime example, and representing a certain navigational challenge, is the under ice in the Arctic/Antarctic or winter/polar environments or deep underwater survey. Among many marine biological substrates, numerous species of macroalgae can be found worldwide in shallow down to 70+ m (clear water) coastal habitats and are essential ecosystem service providers through the habitat they provide for other species, the potential food resource value, and carbon sink they represent. Similarly, corals also provide important ecosystem services through their structure and diversity, are found to harbor increased local diversity, and are equally valid targets as “keystone” species. Hence, we expand current underwater remote sensing methods to combine macroalgal and coral surveys via the development of a multispectral laser serial imager designed for classification via spectral response. By using multiple continuous wave laser wavelength sources to scan and illuminate recreated benthic environments composed of macroalgae and coral, we show how elastic (i.e., reflectance) and inelastic (i.e., fluorescence) spectral responses can potentially be used to differentiate algal color groups and certain coral genus. Experimentally, three laser diodes (450 nm, 490 nm, 520 nm) are sequentially used in conjunction with up to 5 emission filters (450 nm, 490 nm, 520 nm, 580 nm, 685 nm) to acquire images generated by laser line scan pattern via high-speed galvanometric mirrors. Placed directly adjacent to a large saltwater imaging tank fitted with optical viewports, the optical system records target substrate spectral response using a photomultiplier preceded by a filter and is synchronously digitized to the scan rate by a high sample rate Analog-to-Digital Converter (ADC). Acquired images are normalized to correct for imager optical effects allowing for fluorescence intensity-based pixel segmentation via intensity thresholding. Overall, the multispectral laser serial imaging technique shows that the resulting high resolution data can be used for detection and classification of benthic substrates by their spectral response. These methods highlight a path towards eventual pixel-wise spectral response analysis for spectral differentiation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".