Building change detection in very high-resolution remote sensing image based on pseudo-orthorectification
Bibliographic record
Abstract
When using very high-resolution (VHR) remote sensing images acquired at different times to detect building changes, the building positional inconsistencies caused by different satellite imaging angles are an outstanding issue. To tackle this problem, a novel building change detection method based on pseudo-orthorectification (PO) is proposed. First, to determine the building displacement value, a fast line detection method is used to accurately extract the building vertical facade contour lines under the constraint of the Object Space Positioning Consistency (OSPC). Second, the building roof sample selection is automatically conducted under the constraint of building facade contour lines, and the Grab-Cut algorithm is used to extract the roofs combining with corresponding geometric rules. Then, the roof of each building is shifted along the elevation line to its real location. Finally, subtraction is applied to generate the difference image, and reliable change information is obtained by integrating NDVI and shadow information of the building. Three sets of WorldView and QuickBird satellite images are used to compare the proposed method with three state-of-the-art methods. The experimental results show that the average accuracy of the proposed method can reach 92.80%, which is 12.66% higher than that of compared methods.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".