AFUNet With Active Contour Loss for Water Body Detection in SAR Imagery
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Bibliographic record
Abstract
With advancements in remote sensing technology, synthetic aperture radar (SAR) imagery has become one of the main methods to detect surface water bodies. The detection of water bodies in SAR imagery remains a challenging task due to the presence of complex interference. To achieve accurate water body detection, we proposed an attention fusion U-net inspired by the effectiveness of U-net in segmenting small targets with weak edges. First, the spatial attention module and channel attention module are added to the skip connections between encoder and decoder parts to extract useful low- and high-level features, thereby compensating for the loss of semantic information of downsampling. Second, the multiscale convolutional pooling block is introduced into the encoder part to better utilize the contextual information, capturing water and land features at different scales. Third, considering the feature distortion resulting from upsampling, an attentional upsampler (AU) is designed to facilitate lossless feature fusion. Furthermore, an active contour loss is designed as additional regularization to learn more boundary information, improving the model's segmentation performance. The water body detection experiments on the ALOS phased array L-band SAR and Sen1-SAR datasets demonstrate that the presented AFUNet outperforms the related start-of-the-art methods in detection accuracy in terms of five evaluation metrics.
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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 | 0.000 |
| 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