Acta Optica Sinica, Volume. 38, Issue 12, 1228004(2018)

Hyperspectral Target Detection Based on Sparse Representation and Adaptive Model

Feiyan Li*, Hongtao Huo*, Jie Bai, and Wei Wang
Author Affiliations
  • Information Technology and Cyber Security Academy, People's Public Security University of China, Beijing 100038, China
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    Figures & Tables(12)
    (a) Sparse representation without error vector; (b) sparse representation with error vector
    Spectral profiles
    Schematic of similarity calculation
    Synthetic data set. (a) Original target for synthesis; (b) synthetic image; (c) target truth map
    (a) AUC value at different sparsity levels; (b) ROC curves at different λ
    Detection results using different methods. (a) SRTD; (b) SRBBH; (c) SCBD-AWLM; (d) proposed method
    ROC curves corresponding to different methods
    (a) AVIRIS dataset and (b) target truth map
    (a) AUC value at different sparsity levels; (b) ROC curves at different λ
    Detection results for AVIRIS dataset. (a) SRTD; (b) SRBBH; (c) SCBD-AWLM;(d) proposed method
    ROC curves for AVIRIS dataset using different methods
    • Table 1. AUC value for different methods and datasets

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      Table 1. AUC value for different methods and datasets

      MethodAUC
      SyntheticAVIRIS
      SRTD0.56520.7695
      SRBBH0.67520.9327
      SCBD-AWLM0.80780.9119
      Proposed method0.86170.9734
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    Feiyan Li, Hongtao Huo, Jie Bai, Wei Wang. Hyperspectral Target Detection Based on Sparse Representation and Adaptive Model[J]. Acta Optica Sinica, 2018, 38(12): 1228004

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Apr. 23, 2018

    Accepted: Jul. 20, 2018

    Published Online: May. 10, 2019

    The Author Email:

    DOI:10.3788/AOS201838.1228004

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