SAR and optical image fusion for urban infrastructure detection and monitoring
Bibliographic record
Abstract
Spaceborne Synthetic Aperture Radar (SAR) and Optical sensors, are one of the main sources of Earth observation in the present age. Both the data types have their inherent advantages and disadvantages. Spaceborne Optical sensor are restricted by clouds but can offer strong information content in ideal conditions. On the other hand, SAR sensors rely on their own energy and can see through clouds. SAR is potentially an all-weather day/night imager. But SAR sensors have limitations in terms of data collection geometry and algorithmic approximations. Both sensors offer complimentary information for exploitation in data fusion for enhanced results. This research is focused on capitalizing the fusion potential for spaceborne High resolution SAR and Optical data in urban settings. The fusion of high reflection of SAR energy from urban areas and optical features of such areas can be combined to enhance the urban infrastructure detection and monitoring in a SAR/Optical fused scenario. SAR/Optical fusion can take place at three levels 1) pixel level, 2) feature level; and 3) information level. Pixel level fusion is often considered most difficult for high resolution data as precise registration up to subpixel level is required and even slight misregistration results in unfavorable circumstances. Simon Fraser University (SFU) Burnaby Mountain Campus has been chosen for area of interest because of its ongoing student housing and university infrastructure developmental projects. TerraSAR-X High Resolution Spotlight (TSX-HS) Single Look Complex (SLC) images of 1.0 m resolution have continuously being acquired over SFU; along with high resolution Optical (RGB) and Infrared (IR) images (3.0 m resolution each) from “The Planet” acquisitions. Limited high-resolution images from “Google Earth” (GE) in the coinciding period of TSX-HS acquisitions were also acquired for the study. Six fusion techniques have been studied for urban infrastructure detection and have been categorized based on their performance. Precision change maps will be created based on time series analysis for SAR/optical fused data in conjunction with Interferometric SAR (InSAR) analysis to study the long-term effect of urban infrastructure developments over a period of two years.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".