吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (增刊1): 148-153.

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Hyperspectral anomaly detection algorithm based on spectral similarity scale

WANG Yu-lei, ZHAO Chun-hui, QI Bin   

  1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
  • Received:2012-05-25 Published:2013-06-01

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Abstract:

Because conventional anomaly detection algorithms are based on special assumptions,a new algorithm of hyperspectral imagery anomaly detection was presented.Without assuming the background model,first,iterative error analysis (IEA) was used for endmember extraction.Then the Spectral Similarity Scale (SSS) was measured.Through computing the kernel spectral angel cosine (KSAC),the anomaly detection result was obtained.The simulation result shows that the new algorithm can detect the anomalies exactly,and what's more,the new algorithm has the advantages of little computation time and high efficiency.

Key words: hyperspectral, anomaly detection, spectral similarity scale, spectral angel cosine, kernel method

CLC Number: 

  • TP751.1

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