Development of Line-of-Sight Digital Surface Model for Co-Registering Off-Nadir VHR Satellite Imagery With Elevation Data
Bibliographic record
Abstract
Co-registration of very-high-resolution (VHR) images with elevation data is extremely important for many remote sensing applications due to the complementary properties of these two data types. However, this type of multidata source registration has many associated challenges. For instance, although VHR satellite images are usually acquired off-nadir, the integration of off-nadir images with digital surface models (DSMs) for the purpose of urban mapping has been rarely seen in research publications. This is due to the relief displacement of the elevated objects, which causes a problematic misregistration between the perspective off-nadir images and the corresponding orthographic DSMs. Therefore, the co-registration of such datasets is almost impossible unless a true orthorectification process is executed. However, true orthoimages are expensive, time consuming, and difficult to achieve. Thus, this paper proposes a registration method based on developing a line-of-sight DSM solution to effectively register elevation data with off-nadir VHR images. The method utilizes the relevant sensor model in two phases: deriving DSM from stereo images and reprojecting the DSM back to one of the stereo images to generate a line-of-sight DSM for accurate co-registration. To demonstrate the applicability of the proposed method and evaluate the effect of the misregistration, a building detection procedure is implemented. The proposed method is found to be feasible, inexpensive, and of subpixel accuracy. Additionally, it improves the overall accuracy of detecting buildings by almost 12% relative to that when the conventional two-dimensional (2-D) registration technique is used solely due to the elimination of the misregistration effect.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".