A hyperspectral cube consists of a set of images taken at numerous wavelengths. Hyperspectral image data
analysis uses each material’s distinctive patterns of reflection, absorption and emission of electromagnetic energy
at specific wavelengths for classification or detection tasks. Because of the size of the hyperspectral cube, data
reduction is definitely advantageous; when doing this, one wishes to maintain high performances with the least
number of bands. Obviously in such a case, the choice of the bands will be critical. In this paper, we will consider
one particular algorithm, the adaptive coherence estimator (ACE) for the detection of point targets. We give
a quantitative interpretation of the dependence of the algorithm on the number and identity of the bands that
have been chosen. Results on simulated data will be presented.