An Adaptive Infrared Small-Target-Detection Fusion Algorithm Based on Multiscale Local Gradient Contrast for Remote Sensing
Bibliographic record
Abstract
Space vehicles such as missiles and aircraft have relatively long tracking distances. Infrared (IR) detectors are used for small target detection. The target presents point target characteristics, which lack contour, shape, and texture information. The high-brightness cloud edge and high noise have an impact on the detection of small targets because of the complex background of the sky and ground environment. Traditional template-based filtering and local contrast-based methods do not distinguish between different complex background environments, and their strategy is to unify small-target template detection or to use absolute contrast differences; so, it is easy to have a high false alarm rate. It is necessary to study the detection and tracking methods in complex backgrounds and low signal-to-clutter ratios (SCRs). We use the complexity difference as a prior condition for detection in the background of thick clouds and ground highlight buildings. Then, we use the spatial domain filtering and improved local contrast joint algorithm to obtain a significant area. We also provide a new definition of gradient uniformity through the improvement of the local gradient method, which could further enhance the target contrast. It is important to distinguish between small targets, highlighted background edges, and noise. Furthermore, the method can be used for parallel computing. Compared with the traditional space filtering algorithm or local contrast algorithm, the flexible fusion strategy can achieve the rapid detection of small targets with a higher signal-to-clutter ratio gain (SCRG) and background suppression factor (BSF).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".