A Modified DDN Architecture for Enhanced Change Detection in SAR Images
Bibliographic record
Abstract
The field of change detection in synthetic aperture radar (SAR) images has emerged as a critical area of research with diverse applications. This work proposes an innovative approach by integrating convolutional neural networks (CNNs) within a modified dual-domain network (MDDN), simultaneously leveraging both spatial and frequency domain information. Leveraging the multi region convolution module (MRC) in the spatial domain allows the model to adapt and learn from different features present in these diverse regions. To further enhance the efficiency of the proposed approach, the adaptive discrete cosine transform (ADCT) is used in the frequency domain, which plays a pivotal role in selecting critical components. The ADCT incorporates an attention gate mechanism which involves using element-wise multiplication to focus on key elements within the information vector. Simulations convey that the proposed method works well on the ‘Ottawa’ and ‘Sulzberger’ dataset, but the ‘Yellow River-A’ and ‘Yellow River-C’ datasets result in suboptimal performance. Also, the MDDN gives better performance for ‘PCC’ and ‘Kappa’ coefficient.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".