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31 August 2009 An ad-hoc approach for quality assessment of hyperspectral datacubes in target detection
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This paper addresses assessment of different processing techniques for hyperspectral images target detection. An ad-hoc quality assessment approach is adopted to compare different noise reduction techniques of hyperspectral images for target detection applications. Two different noise reduction techniques are applied to a datacube collected over a well-studied area with human made targets. The quality of these noise reduced datacubes in preserving the identity of the targets of interest is compared with that of the original datacube. This is achieved by applying different measures on the datacubes. First, the Virtual Dimensionality is used and the results for both of the noise reduction methods are compared with those of the original datacube for several false-alarm probabilities. Then Maximum Noise Fraction is applied to the datacubes and its capability in finding a transform in which the information of the datacube is represented in a smaller number of bands is assessed. Finally using set measures and knowing the location of the targets, different classes are defined and the intraclass and interclass distances for each datacube is measured.
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Reza Rashidi Far and Shen-en Qian "An ad-hoc approach for quality assessment of hyperspectral datacubes in target detection", Proc. SPIE 7455, Satellite Data Compression, Communication, and Processing V, 74550W (31 August 2009);

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