A Review on Various Shadow Detection and Compensation Techniques in Remote Sensing Images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Remote sensing images provide a valuable source of information about the earth's surface. The presence of shadow can reduce the amount of information that can be extracted from these images. Shadow in remote sensing images is produced due to blockage of a direct light by an object. In spite of the reflectance gathered in the shadow area being weak, there is still valuable information that makes shadow restoration possible. Shadow restoration process consists of 2 main steps: detection and compensation. Various algorithms and methods have been developed to perform these 2 steps. These algorithms differ according to the objects causing shadow and types of sensor. Consequently, it is important to review the different approaches that have been employed in shadow correction research to delineate their suitability for a specific application. This article is aimed at reviewing various shadow detection and compensation techniques with their methods of evaluation, taking into consideration objects causing shadow and type of sensor used. Also, it gives discussion and recommendations to enhance the performance of existing methods.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 it