Performance Evaluation of Change Detection in SAR Images Based on Hybrid Antlion DWT Fuzzy c-Means Clustering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In this paper, the main objective is to detect changes in the geographical area of Ottawa city in Canada due to floods. Two multi-temporal Synthetic Aperture Radar (SAR) images have been taken to evaluate the un-supervised change detection process. In this process, two ratio operators named as Log-Ratio and Mean-Ratio are used to generate a difference image. Performing image fusion based on DWT by selecting optimum filter coefficients by satisfying the wavelet filter coefficient properties through a novel image fusion technique is named as ADWT. GA, PSO, AntLion Optimization algorithms (ALO) and Hybridized AntLion Algorithm (HALO) have been adapted to perform the ADWT based image fusion. Segmentation has been performed based on fuzzy c-Means clustering to detect changed and unchanged pixels. Finally, the performance of the proposed method will be analysed by comparing the segmented image with the ground truth image in terms of sensitivity, accuracy, specificity, precision, F1-score.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it