Towards transparent deep learning for surface water detection from SAR imagery
Bibliographic record
Abstract
Water detection from SAR imagery has significant values, such as the flood monitoring and environmental protection. Nowadays, significant progress has been achieved in water detection using deep neural network (DNN) methods, but the blackbox behavior incurs many doubts in the performance of deep learning techniques, which undermines its trustworthiness in water detection from SAR imagery. By integrating SAR domain knowledge, DNN and eXplainable Artificial Intelligence (XAI), an explainable DNN framework for surface water detection is proposed for the first time. This framework includes three parts: the water extraction network containing four backbone networks, the Local and Global Mixed Attribution (LGMA) module for performance evaluation of backbone network, and the Semantic Specific-class Activation Mapping (SSAM) module, which performs geo-visualization for the output layers of high-level features. In the experiment, SAR images from different resolutions and frequency-bands are utilized, which are from millimeter-wave and Sentinel-1 systems. The attribution maps and heatmaps of four backbone networks are assessed towards the final water extraction results. The experiment indicates that the proposed framework can glass-box the decision-making process of DNN in water detection and offer corresponding attribution analytics for given input SAR imagery. This work encourages other scholars to conduct extensive research on the explanation of DNN in SAR domain, gradually establish the trustworthiness of DNN, and promote the development of DNN in SAR images analytics.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".