Mapping sub‐pixel fluvial grain sizes with hyperspatial imagery
Bibliographic record
Abstract
Abstract This study presents an investigation of image texture approaches for mapping sub‐pixel fluvial grain‐size features from airborne imagery, allowing for the rapid acquisition of surface sand and coarse fraction (>1·41 mm) grain‐size information. Imagery at 30 mm resolution was acquired over four gravel bars from the Fraser River (British Columbia, Canada). Combined first‐order and second‐order image texture approaches (windowed standard deviation filter and the grey level co‐occurrence matrix) were used. First‐order image texture, through the application of a standard deviation filter and subsequent thresholding was used to detect the presence of surface sand, with optimal accuracy achieved at 91 ± 1·9%. A wide‐ranging parameter space investigation was used to derive optimum parameters for the grey‐level co‐occurrence matrix. Subsequently first‐order and second‐order image textures were used in multiple linear regression to achieve good calibrations with several sub‐pixel grain‐size percentiles; relative error at 1·44%, 3·18%, 6·80% and 10·6% for D 5 , D 16 , D 35 and D 50 , respectively. The larger percentiles of D 84 and D 95 had relative errors of 24·7% and 29·7%, respectively. The breakdown of calibration precision for larger percentiles is attributed to a ‘pixel averaging effect’. It is concluded that multispectral imagery is not required, because sufficient image texture information can be derived from standard colour imagery. Recommendations are suggested for the application of this method to other localities and data sets, thus reducing exhaustive parameter searches in future studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".