LRR-Net: An Interpretable Deep Unfolding Network for Hyperspectral Anomaly Detection

Considerable endeavors have been expended towards enhancing the representation performance for Hyperspectral Anomaly Detection (HAD) through physical model-based methods and recent deep learning-based approaches. Of these methods, the Low-Rank Representation (LRR) model is widely adopted for its formidable separation capabilities for background and target features, however, its practical applications are limited due to the reliance on manual parameter selection and subpar generalization performance. To this end, this paper presents a new HAD baseline network, referred to as LRR-Net, which synergizes the LRR model with deep learning techniques. LRR-Net leverages the alternating direction method of multipliers…

• NN
  National Natural Science Foundation of China

  Awards: 42271350, 42241109
• AR
  AXA Research Fund
• NK
  National Key Research and Development Program of China

  Award: 2022YFB3903401