Hyperspectral Anomaly Detection Using a Siamese Spatial Feature with Weakly Supervised Learning
Hyperspectral anomaly detection processes background and anomalous targets in spectral data using an unsupervised approach.However,the complex nature of hyperspectral background distributions and the presence of anomalous targets in the training samples challenge the model's generalization and application capabilities.To address this issue,we propose the S2FDNet detection network that integrates sample self-learning with dual feature fusion.First,an anomaly background category search algorithm based on measure K-means was employed to classify the background and anomaly rough labels under weak supervision.A dual spectral and spatial feature extraction framework,including a global-local spectral feature extraction module and a multiscale spatial feature extraction module,was then employed to enhance the discriminative capacity for background and anomalous features across high-dimensional spaces.The model underwent updates to abnormal and background sample sets and model parameters during the weakly supervised training mode,and anomalies were directly detected using predicted probabilities during testing.Evaluations with two hyperspectral datasets confirm that S2FDNet algorithm effectively identifies anomalous targets and improves the distinction between background and anomalies.
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