Hyperspectral Anomaly Detection Using Dual-Branch Network Based on Frequency Domain Learning
Bibliographic record
Abstract
Existing deep learning-based hyperspectral anomaly detection methods often overlook frequency domain features, hindering the ability to effectively distinguish between background and anomalies. Furthermore, many methods directly apply Mahalanobis distance to reconstructed hyperspectral image (HSI) for detection, disregarding the structural features of the original HSI. This leads to insufficient representation of important information and ultimately limits detection performance. To resolve these challenges, this paper presents a dual-branch network based on frequency domain learning (DB-FDLNet). Using the Haar wavelet transform, the original HSI is divided into highand low-frequency components. Based on the edge detailed properties of the high-frequency component and the smoothness of the low-frequency component, distinct network branches are designed for feature extraction, with the extracted features fused for HSI reconstruction. Notably, the detection process utilizes the outputs of the high- and low-frequency branches directly, rather than the reconstructed HSI. To fully leverage the key structural information in the original HSI, a Mahalanobis distance detection method incorporating the structural similarity index is proposed. By weighting the covariance matrix, the method enhances critical structural and spectral features, improving detection accuracy while effectively suppressing noise. Experiments on six datasets demonstrate the proposed method's superiority and robustness over eight advanced hyperspectral anomaly detection methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
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".