Performance of the selected algorithm is crucial for hyperspectral target detection. However, it is sure that there is no
algorithm that is appropriate for all hyperspectral data or applications. Result of detection can be affected by many
factors, including spectral feature of targets and backgrounds, characteristic of spectrometer and pre-processing method.
Thus to improve practicability of hyperspectral target detection, studies should not be focused solely on the algorithms,
but also be extended to discussion of above affecting factors. In this paper, hyperspectral imagery measurements and
data processing are studied. Six aspects including paints, spectral response, imaging spectrometer mode, image noise and
dimension reduction are discussed. Typical abnormity and matching algorithm are applied on images simulated with
different factors. The influences of these factors on target detection are analyzed. At last, this research provides a
strategy for precision control of target detection in hyperspectral imagery. Researches in this paper can be of great help
for algorithm development and algorithm selection in diversified hyperspectral data and applications.