PSRN: Polarimetric Space Reconstruction Network for PolSAR Image Semantic Segmentation
Bibliographic record
Abstract
To accurately extract various ground objects from PolSAR images is a challenging research topic. The deep convolutional neural networks are widely used in SAR segmentation due to their remarkable performance in optical remote sensing images. However, they are still limited by the geometric deformation of the objects, the strong scattering interference among adjacent objects and the difficulty in distinguishing similar things. A large part of the reason lies in the insufficient utilization and the damage of feature mining for PolSAR data. In this paper, focusing on the scattering matrix and the polarimetric coherency matrix, a polarimetric space reconstruction network is proposed. First, to maintain the relatively initial spatial constraints and the complete polarimetric information, the inputs are arranged by a spatial amplification coding method for the polarimetric coherency matrix and scattering matrix. Second, a statistics enhancement module based on scattering characteristics is proposed to mine the differential expression among multiple scattering and polarimetric components, which supplements the local feature representation of convolutional operators. Third, the designed dual self-attention mechanism can capture the amplitude and phase relations of matrix element context adaptively. The proposed method keeps the spatiality of each scattering vector and fully unearths the complementary information involved in the full polarimetric data. Moreover, it accomplishes the accurate land cover classification for PolSAR images, especially the traditional confusing categories, such as water and roads. The experimental results on the E-SAR, AIRSAR, Gaofen-3 and RADARSAT-2 datasets show that the proposed method is effective and promising for PolSAR image segmentation.
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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 | 0.001 |
| 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 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".