This paper proposes a new method to extract the endmembers of a hyperspectral datacube using the geometry of the
datacube. The criterion used to find the endmembers in this method is the volume of the simplex. Unlike to the widely
used endmember extraction method "N-FINDR", which calculates the volume of a simplex as many times as the number
of the vertices of the simplex for each pixel of the datacube in searching for the replacers for the vertices, the proposed
method calculates the volume only once for each pixel of the datacube by taking into account of the geometry of the
hyperspectral datacube that is tackled. For each pixel, the proposed method finds the closest vertex of the simplex to that
pixel. Then the closest vertex is replaced with the pixel for updating the simplex. Computational complexity of the
proposed method is one order of magnitude less than the N-FINDR. As the proposed method is using the same criterion
as N-FINDR we refer it to as fast N-FINDR (FN-FINDR). The performance of the proposed method was compared with
N-FINDR using an AVIRIS datacube and a HYDICE datacube. The performance of the proposed method was evaluated
using three different distance measures. The comparison was also made using two different dimensionality reduction
methods. It is observed that the FN-FINDR with a modified Euclidean distance works as well as N-FINDR.
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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