Remote Sensing Image Segmentation of Pipeline High Consequence Area Based on Bee Colony Strategy Fuzzy MRF Algorithm
Bibliographic record
Abstract
Oil pipeline is a kind of high-risk continuous transportation system. High consequence area refers to the area where public life as well as property are endangered and even the environment is polluted after pipeline leakage. Through the analysis of remote sensing images, the position of oil pipeline and the change of its surrounding environment can be determined, and the monitoring and protection of oil pipeline in high consequence area can be realized. Aiming at the problems of low segmentation accuracy, difficulty in obtaining global optimal solution and low efficiency caused by prior knowledge of classical Markov image segmentation. A fuzzy Markov random field algorithm based on artificial bee colony strategy is proposed. Firstly, according to the initial image segmentation results, pixels are divided into definite points and fuzzy points, and only fuzzy points are calculated. Secondly, a Markov algorithm based on artificial bee colony strategy is designed, which can adaptively select potential function parameters for different images. Finally, the improved algorithm is applied to remote sensing image segmentation in high consequence area of oil pipeline. By comparing multiple images, performance parameters and algorithms, it is proved that the improved algorithm has better optimization ability and convergence performance.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".