International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
    
  
  
  
   
   
   
   
   
  
  
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
  
Conclusion 
Combining the robust Manhanlobis anomaly detection methods and nonlinear mixture models, 
a robust metric based anomaly detection method in kernel feature space is proposed. Experiments 
reveal that the proposed method does provide a more reliable and robust metric for anomaly 
detection from hyperspectral remote sensing images, especially for detecting the ones on resolved 
pixels. 
Reference 
[1] B. Du and L. Zhang, “Random Selection based Anomaly Detector for Hyperspectral 
Imagery,” IEEE Trans. Geosci. Remote Sens., vol. 49, no. 5, pp. 1578 — 1589, 2011. 
[2] I. S. Reed and X. Yu, “Adaptive multiple-band CFAR detection of an optical pattern with 
unknown spectral distribution,” IEEE Trans. Acoust., Speech, Signal Process, vol. 38, no. 10, 
pp.1760—1770, 1990. 
[3] T. E. Smetek and K.W. Bauer, “A Comparison of Multivariate Outlier Detection Methods 
for Finding Hyperspectral Anomalies,” Military Operations Research, vol. 13, no. 4, pp. 19-44, 
2008. 
[4] T. E. Smetek, “Hyperspectra Improved Anomaly Detection and Signature Matching 
Methods,” Ph.D. dissertation, Air Univ., Ohio, 2007. 
[5] H. Kwon and N. M. Nasrabadi, “Kernel RX-Algorithm: A Nonlinear Anomaly Detector for 
Hyperspectral Imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 2, 
pp. 388-397, 2005. 
[6] D. Manolakis and G. Shaw, “Detection Algorithms for Hyperspectral Imaging Applications,” 
IEEE Signal Processing Mag., vol. 19, no. 1, pp. 29-43, Jan. 2002. 
[7] C.-I. Chang and S.-S. Chiang, “Anomaly Detection and Classification for Hyperspectral 
Imagery,” IEEE Trans. Geosci. Remote Sens., vol. 40, no. 6, pp. 1314-1325, 2002. 
[8] J. Plaza, P. Martinez, R. Perez, and A. Plaza, “Nonlinear neural network mixture models for 
fractional abundance estimation in AVIRIS hyperspectral images," in Proc. XIII JPL Airborne 
   
Earth Science Workshop, Pasadena, CA, 2004.