Synthetic aperture lidar as a future tool for earth observation
Bibliographic record
Abstract
Synthetic aperture radar (SAR) is a tool of prime importance for Earth observation; it provides day and night capabilities in various weather conditions. State-of-the-art satellite SAR systems are a few meters in height and width and achieve resolutions of less than 1 m with revisit times on the order of days. Today’s Earth observation needs demand higher resolution imaging together with timelier data collection within a compact low power consumption payload. Such needs are seen in Earth Observation applications such as disaster management of earthquakes, landslides, forest fires, floods and others. In these applications the availability of timely reliable information is critical to assess the extent of the disaster and to rapidly and safely deploy rescue teams. Synthetic aperture lidar (SAL) is based on the same basic principles as SAR. Both rely on the acquisition of multiple electromagnetic echoes to emulate a large antenna aperture providing the ability to produce high resolution images. However, in SAL, much shorter optical wavelengths (1.5 μm) are used instead of radar ones (wavelengths around 3 cm). Resolution being related to the wavelength, multiple orders of magnitude of improvement could be theoretically expected. Also, the sources, the detector, and the components are much smaller in optical domain than those for radar. The resulting system can thus be made compact opening the door to deployment onboard small satellites, airborne platforms and unmanned air vehicles. This has a strong impact on the time required to develop, deploy and use a payload. Moreover, in combination with airborne deployment, revisit times can be made much smaller and accessibility to the information can become almost in real-time. Over the last decades, studies from different groups have been done to validate the feasibility of a SAL system for 2D imagery and more recently for 3D static target imagery. In this paper, an overview of the advantages of this emerging technology will be presented. As well, simulations and laboratory demonstrations of deformation mapping using a tabletop synthetic aperture lidar system operated at 1.5 μm are reviewed. The transmitter and receptor of the fiber-based system are mounted on a translation stage which move at a constant speed relatively to the target (sand) located 25 cm away. The change in the 3D profile of the target is thereafter monitored with sub-millimeter precision using the multiple-pass SAL system. Results obtained with a SAL laboratory prototype are reviewed along with the potential applications for Earth observation.
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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 | 0.000 |
| 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".