Change Detection in Synthetic Aperture Radar Images based on a Spatial Pyramid Pooling Attention Network (SPPANet)
Why this work is in the frame
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Bibliographic record
Abstract
Synthetic aperture radar (SAR) plays a vital role in change detection (CD) analysis due to the ability to produce remote sensing images throughout the day, regardless of weather conditions. Nowadays, deep learning methods have gained popularity in multitemporal SAR image CD applications. However, false labels generated during the preclassification stage limit the performance of the CD process. This work employs a fast and robust fuzzy c-means clustering to generate the pseudo-label samples and lightweight spatial pyramid pooling attention network (SPPANet) to detect changes in multitemporal SAR images. The spatial pyramid structure in SPPANet applies adaptive pooling layers to provide better contextual information without incurring computational overhead. The log-ratio operator is used to generate the difference image (DI), and the pseudo-label samples are created from DI. The pseudo-label samples are used to create the training and testing patches. Finally, the trained SPPANet is used to classify testing samples into unchanged and changed classes. The SPPANet achieves an accuracy of 98.70%, 99.06%, 96.40%, and 99.10% for Ottawa, San Francisco, Yellow River, and Farmland datasets, respectively.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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