Local anomaly detection algorithm based on sliding windows in spectral space
Bibliographic record
Abstract
In this paper, a novel local ways to implement hyperspectral anomaly detector is presented. Usually, the local detectors are implemented in the spatial window of image scene, but the proposed approach is implemented on the windows of spectral space. As a multivariate data, the hyperspectral image datasets can be considered as a low-dimensional manifold embedded in the high-dimensional spectral space. In real environments, nonlinear spectral mixture occurs more frequently. At these situations, whole dataset would be distributed in one or more nonlinear manifolds in high dimensional space, such as a hyper-curve surface or nonlinear hyper-simplex. However, the majority of global and local detectors in hyperspectral image are based on the linear projections. They are established on the assumption that the geometric distribution of datasets is a linear manifold. It is incapable for them to deal with these nonlinear manifold data, even for spatial local data. In this paper, a novel anomaly detection algorithm based on local linear manifold is put forward to handle the nonlinear manifold problems. In the algorithm, the local neighborhood relationships are established in spectral space, and then an anomaly detector based on linear projection is carried out in these local areas. This situation is similar to using sliding windows in the spectral space. The results are compared with classic spatial local algorithm by using real hyperspectral image and demonstrate the effectiveness in improving the weak anomalies detection and decreasing the false alarms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 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".