Transformation for target detection in hyperspectral imaging

Edisanter Lo; Emmett Ientilucci

doi:10.1117/12.2263887

5 May 2017 Transformation for target detection in hyperspectral imaging

Edisanter Lo, Emmett Ientilucci

Proceedings Volume 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII; 101980Z (2017) https://doi.org/10.1117/12.2263887
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

Conventional algorithms for target detection in hyperspectral imaging usually require multivariate normal distributions for the background and target pixels. Significant deviation from the assumed distributions could lead to incorrect detection. It is possible to make the non-normal pixels into more normal-looking pixels by using a transformation on the pixels. A multivariate transformation based maximum likelihood is proposed in this paper to improve target detection in hyperspectral imaging. Experimental results show that the distribution of the transformed pixels become closer to a multivariate normal distribution and the performance of the detection algorithms improves after the transformation.

Conference Presentation

Citation Download Citation

Edisanter Lo and Emmett Ientilucci "Transformation for target detection in hyperspectral imaging", Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101980Z (5 May 2017); https://doi.org/10.1117/12.2263887

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