Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 9, Issue 2, Pages 732-743Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2015.2487638
Keywords
Gaussianity measure; hyperspectral imaging (HSI); Reed Xiaoli (RX) algorithm; target energy
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We evaluate the anomaly detection performance of a hyperspectral detection algorithm based on selecting specific bands of the hyperspectral cube. The best bands to be chosen are, as expected, based on the degree that the resulting target and background signatures differ from one another, which is determined by the target energy in the whitened space. In addition, we show that the closer the background distribution resembles a Gaussian model, the better the band performs in the detection algorithm. A comparison is made between choosing bands on the basis of their similarity to Gaussian distribution and one based on minimum variance. Examples are provided both on theoretical simulations and on experimental data.
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