Mapping forest stands using RADARSAT-2 quad-polarization SAR images: A combination of polarimetric and spatial information
Bibliographic record
Abstract
This study proposes an approach which simultaneously uses spatial information and polarimetric data from a RADARSAT-2 quad-polarization satellite image for forest tree species classification. The study area is near the Gounamitz River located in northwestern New Brunswick (Canada). After geometric correction of the image, two statistical models were used for the classification: (1) a Markov random fields model based on an initial segmentation provided by the K-means algorithm to account for the spatial statistical dependencies between adjacent sites; and (2) a K-distribution model with, as parameters, the covariance matrix containing all of the polarimetric information. The classification was optimized using the stochastic simulated annealing algorithm. Validation of the results was performed by comparison with field inventory measurements. Variation of the backscattering coefficient c° obtained for the RADARSAT-2 quad-polarization SAR image with incidence angles of 26 0 and 45 ° ranged from 1 and 3 dB for the different tree species. The results of average and overall accuracies of the classification were respectively 77.13% and 72.35% for the 26° incidence angle image compared to 81.47% and 79.12% for the 45°incidence angle.
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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) | 0.000 | 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".