A radargrammetric model for high resolution SAR imagery
Bibliographic record
Abstract
Digital Surface and Terrain Models (DSMs/DTMs) have large relevance in some territorial applications, such as topographic mapping, spatial and temporal change detection, feature extraction and data visualization. DSMs/DTMs extraction from satellite stereo pair offers some advantages, among which low cost, speed of data acquisition and processing, surveys of critical areas, easy monitoring of wide areas, availability of several commercial software and algorithms for data processing. In particular, the DSMs generation from Synthetic Aperture Radar (SAR) imagery offers the significant advantage of possible data acquisition during the night and in presence of clouds. \nThe availability of new high resolution SAR spaceborne sensors as COSMO-SkyMed (Italian), TerraSAR-X (German) and RADARSAT-2 (Canadian) offers new interesting potentialities for the acquisition of data useful for the generation of DSMs following the radargrammetric approach, based at least on a couple of images of the same area acquired from two different points of view as for the standard photogrammetry applied to optical imagery.\nThe aim of this work was the development and the implementation of an original rigorous radargrammetric model for the orientation of SAR imagery, suited for the subsequent DSM generation. The model performs a 3D orientation based on two range and two zero-Doppler equations starting from SAR stereo pairs in slant range and zero-Doppler projection, acquired in SpotLight mode, that is at the highest resolution presently available (1 m ground resolution).\nThe model has been implemented in SISAR (Software per Immagini Satellitari ad Alta Risoluzione), a scientific software developed at Geodesy and Geomatic Institute of the University of Rome “La Sapienza”. This software was at first devoted to the orientation of high resolution optical imagery, and in the last year it has been extended also to SAR imagery. \nMoreover a tool for the SAR Rational Polynomial Coefficients (RPCs) generation has been implemented in SISAR software, similarly to the one already developed for the optical sensors. \nThe possibility to generate RPCs starting from a rigorous model sounds of particular interest since, at present, the most part of SAR imagery is not supplied with RPCs, although the Rational Polynomial Functions (RPFs) model is available in several commercial software. The RPCs can be an useful tool in place of the rigorous model in processes as the image orthorectification/geocoding or the DSMs generation, since the RPFs model is very simple and fast to be applied.\nThe model implemented has been tested on COSMO-SkyMed and on TerraSAR-X SpotLight imagery, showing that a vertical accuracy at level of better than 3 m is achievable even with quite few Ground Control Points.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".