Unsupervised change detection of VHR remote sensing images based on multi-resolution Markov Random Field in wavelet domain
Bibliographic record
Abstract
This paper proposes an unsupervised change detection method for very-high-resolution (VHR) remote sensing images based on multi-resolution Markov random field (MRF) model in wavelet domain. Firstly, the wavelet transform is performed on the difference image achieved by the change vector analysis (CVA) method, and the wavelet coefficients at each scale are obtained. Then, MRF model is constructed based on the wavelet coefficients. The wavelet high-frequency coefficients establish a feature field model that describes the feature attributes of each pixel location at each scale. The initial change map (changed and unchanged) at the coarse scale are generated through applying the k-means method to the wavelet low-frequency coefficients, and a label field model describing the region of the variation results is established. The label and feature field, at the same scale, got the optimized change map under the Bayesian criterion. Finally, the results of the low-resolution scale change map are directly projected as the adjacent higher-scale initial change map. The more accurate change map is obtained successively from the coarse scale to the original resolution scale, and the detection result of the original resolution is obtained at last. Experiments on Quick Bird, SPOT-5, and IKONOS optical images have demonstrated the effectiveness of the proposed method. The experimental results show that the method has better regional consistency and strong robustness.
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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 | 0.002 | 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) | 0.000 | 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".