Exploiting Structured Sparsity for Hyperspectral Anomaly Detection

Fei Li, Xiuwei Zhang, Lei Zhang, Dongmei Jiang, Yanning Zhang

Research output: Contribution to journalArticlepeer-review

56 Scopus citations

Abstract

Sparse representation-based background modeling facilitates much recent progress in hyperspectral anomaly detection (AD). The sparse representation of background often exhibits underlying structure, which is crucial to distinguish between background and anomaly. However, how to exploit such underlying structure is still challenging. To address this problem, we present a novel hyperspectral AD method, which can exploit the structured sparsity in modeling the background more accurately. With the plausible background area detected by a local RX detector, a robust background spectrum dictionary is learned in a principal component analysis way. A reweighted Laplace prior-based structured sparse representation model is then employed to reconstruct the spectrum of each pixel. With considering the structured sparsity in representation, the background pixels can be reconstructed more accurately than the anomaly ones, which thus can be detected based on the reconstruction error. To further improve the detection performance, an intracluster reconstruction model is developed to exploit the spatial similarity among the background pixels in the same cluster. The anomaly pixels can then be detected based on the cost of intracluster reconstruction error. By linearly combining these two detection results, improvement is obviously achieved on detection accuracy. Experimental results on both simulated and real-world data sets demonstrate that the proposed method outperforms several state-of-the-art hyperspectral AD methods.

Original languageEnglish
Pages (from-to)4050-4064
Number of pages15
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume56
Issue number7
DOIs
StatePublished - Jul 2018

Keywords

  • Anomaly detection (AD)
  • intracluster reconstruction
  • reweighted Laplace prior
  • structured sparse representation

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