Change detection in remote sensing image using a modified logarithmic mean-based thresholding
Bibliographic record
Abstract
In this paper, we propose a novel approach to change detection in remote sensing imagery by modifying the logarithmic mean-based thresholding technique (MLMBTICD). This method introduces a preprocessing step using a mean filter to enhance the accuracy of detecting changes between multi-temporal satellite images. The mean filter reduces noise and smoothens the images before calculating the logarithmic difference, which improves the quality of the change detection process. The proposed approach was tested on two benchmark datasets: the Onera dataset, which contains satellite images of urban regions, and the Ottawa dataset, consisting of RADARSAT-2 images. The effectiveness of the MLMBTICD method was evaluated using Overall Accuracy (OA) and Kappa metrics. The results demonstrate that our method achieves better performance compared to the original logarithmic thresholding method, yielding improved change detection accuracy. The preprocessing step significantly enhances the quality of the detected changes, making the proposed method a robust and efficient solution for various remote sensing applications, including land use monitoring, urban development, and environmental change analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| 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 | 0.001 |
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".