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.
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 (Presented at SPIE Defense + Security: April 12, 2017; Published: 5 May 2017); https://doi.org/10.1117/12.2263887.
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