An Object-Based Markov Random Field Model with Anisotropic Penalty for Semantic Segmentation of High Spatial Resolution Remote Sensing Imagery
Bibliographic record
Abstract
The Markov random field model (MRF) has attracted a lot of attention in the field of remote sensing semantic segmentation. But, most MRF-based methods fail to capture the various interactions between different land classes by using the isotropic potential function. In order to solve such a problem, this paper proposed a new generalized probability inference with an anisotropic penalty for the object-based MRF model (OMRF-AP) that can distinguish the differences in the interactions between any two land classes. Specifically, an anisotropic penalty matrix was first developed to describe the relationships between different classes. Then, an expected value of the penalty information (EVPI) was developed in this inference criterion to integrate the anisotropic class-interaction information and the posteriori distribution information of the OMRF model. Finally, by iteratively updating the EVPI terms of different classes, segmentation results could be achieved when the iteration converged. Experiments of texture images and different remote sensing images demonstrated that our method could show a better performance than other state-of-the-art MRF-based methods, and a post-processing scheme of the OMRF-AP model was also discussed in the experiments.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".