A self-adapted threshold-based region merging method for remote sensing image segmentation
Why this work is in the frame
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Bibliographic record
Abstract
Remote sensing image segmentation is the critical process in the workflow of object-based image analysis. Recently, region merging methods have attracted growing attention because they are able to utilize more features than spectral signals derived from initial segments. However, the existing algorithms commonly use fixed parameters to control the process of region merging, which limits the possibility of the co-existence of large and small segments. To address this issue, we propose a self-adapted region merging method, based on spectral angle threshold, toward segmenting remote sensing images. This method involves two steps: i) multi-band watershed transformation to initiate primitive segments and ii) self-adapted threshold-based region merging. The performance of the proposed algorithm is evaluated in a farmland division and compared to the existing region merging method implemented in SAGA. The results reveal the proposed segmentation method outperforms the SAGA method, as indicated by its lower discrepancy measure.
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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) | 0.000 | 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 it