Classification of Seabed Sediment by Combining Airborne LiDAR Bathymetry and Multispectral Remote Sensing Images
Bibliographic record
Abstract
The classification of seabed sediment plays an important role in marine ecological environment protection and other related fields. To fully explore the application ability of marine geographic information in seabed sediment classification, this article makes a contribution to overcome the low accuracy and reliability shortcomings of using single data source and traditional classifiers. Based on extracted multisource features, the scale-invariant feature transform - random sample consensus model is applied to realize feature-level fusion between airborne LiDAR bathymetry (ALB) data and multispectral remote sensing images. Furthermore, a dual-branch convolutional neural network (CNN) classifier is constructed to classify the seabed sediment into five categories (coral reef, sand, gravel, coastal zone, and vegetation). To verify the effectiveness of fused data in seabed sediment classification, experiments were conducted using multispectral remote sensing images and ALB data. Experimental results show that the overall classification accuracy and the Kappa coefficient of the dual CNN classifier constructed in this article are 98.2% and 0.977, respectively. In addition, the classification results using multisource fusion data are higher than those using single-source data, indicating the accuracy and effectiveness of multisource fusion features for classification. The research results can provide effective technical support for seabed sediment classification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".