Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation

A novel method for anomaly detection in hyperspectral images (HSIs) is proposed based on low-rank and sparse representation. The proposed method is based on the separation of the background and the anomalies in the observed data. Since each pixel in the background can be approximately represented by a background dictionary and the representation coefficients of all pixels form a low-rank matrix, a low-rank representation is used to model the background part. To better characterize each pixel's local representation, a sparsity-inducing regularization term is added to the representation coefficients. Moreover, a dictionary construction strategy is adopted to make the dictionary more stable and discriminative.…

• NN
  National Natural Science Foundation of China

  Awards: 61471199, 11431015
• FR
  Fundamental Research Funds for the Central Universities

  Award: 30915012204