Estimation of surface topography from single polarimetric SAR imagery using RADARSAT2
Bibliographic record
Abstract
In this paper a practical method is demonstrated for estimating terrain slopes in azimuth and ground range directions for digital elevation model (DEM) generation without any prior knowledge on the terrain by using only one single pass of polarimetric synthetic aperture radar (PolSAR) instead of two-pass or interferometric SAR (INSAR). The basic approach is by combing the orientation angle estimation and a shape-from-shading technique (SFS) which is mostly used by the computer vision community. In particular, when limited PolSAR data are available, this technique provides an alternative way for DEM generation. The polarization orientation angle (POA) is related to both the range and azimulh angles of the tilted surface and radar viewing angle and it can be estimated from the PolSAR data by using the circular polarization method which shows the best performance in computation efficiency and accuracy with respect to the other methods. After terrain slopes in both the range and azimuth directions have been estimated initially by the combination of the POA estimation and the SFS algorithm, a least squares method similar to that used in interferometric phase unwrapping is used to generate the topography. The least squares approach to phase unwrapping obtains an unwrapped solution by minimizing the differences between the discrete partial derivatives of the (wrapped) phase data and the discrete partial derivatives of the unwrapped solution. We illustrate our results by using the polarimetric SAR images acquired in Algeria by the RadarSAT2 (FQ19) in C-band.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".