  In the small target detection problem a pattern to be located is on the order
of magnitude less numerous than other patterns present in the dataset. This
applies both to the case of supervised detection, where the known template is
expected to match in just a few areas and unsupervised anomaly detection, as
anomalies are rare by definition. This problem is frequently related to the
imaging applications, i.e. detection within the scene acquired by a camera. To
maximize available data about the scene, hyperspectral cameras are used; at
each pixel, they record spectral data in hundreds of narrow bands.
  The typical feature of hyperspectral imaging is that characteristic
properties of target materials are visible in the small number of bands, where
light of certain wavelength interacts with characteristic molecules. A
target-independent band selection method based on statistical principles is a
versatile tool for solving this problem in different practical applications.
  Combination of a regular background and a rare standing out anomaly will
produce a distortion in the joint distribution of hyperspectral pixels. Higher
Order Cumulants Tensors are a natural `window' into this distribution, allowing
to measure properties and suggest candidate bands for removal. While there have
been attempts at producing band selection algorithms based on the 3 rd
cumulant's tensor i.e. the joint skewness, the literature lacks a systematic
analysis of how the order of the cumulant tensor used affects effectiveness of
band selection in detection applications. In this paper we present an analysis
of a general algorithm for band selection based on higher order cumulants. We
discuss its usability related to the observed breaking points in performance,
depending both on method order and the desired number of bands. Finally we
perform experiments and evaluate these methods in a hyperspectral detection
scenario.
