Concatenated Deep-Learning Framework for Multitask Change Detection of Optical and SAR Images
Why this work is in the frame
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Bibliographic record
Abstract
Optical and synthetic aperture radar (SAR) images provide complementary information to each other. However, the heterogeneity of same-ground objects brings a large difficulty to change detection (CD). Correspondingly, transformation-based methods are developed with two independent tasks of image translation and CD. Most methods only utilize deep learning for image translation, and the simple cluster and threshold segmentation leads to poor CD results. Recently, DTCDN was proposed to apply deep learning for image translation and CD to improve the results. However, DTCDN requires the sequential training of the two independent subnetwork structures with a high computational cost. Towards this end, a concatenated deep learning framework, multi-task change detection network (MTCDN), of optical and SAR images is proposed by integrating change detection network into a complete generative adversarial network (GAN). This framework contains two generators and discriminators for optical and SAR image domains. Multi-task refers to the combination of image identification by discriminators and CD based on an improved UNet++. The generators are responsible for image translation to unify the two images into the same feature domain. In the training and prediction stages, an end-to-end framework is realized to reduce cost. The experimental results on four optical and SAR datasets prove the effectiveness and robustness of the proposed framework over eight baselines. The code is available at https://github.com/lixinghua5540/MTCDN.
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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