Real-time or near real-time hyperspectral detection and identification are
extremely useful and needed in many fields. These data sets can be quite large,
and the algorithms can require numerous computations that slow the process
down. A common way of speeding up the process is to use principal component
analysis (PCA) for dimension reduction. In the reduced dimensional space,
provided by a subset of the principal components, fewer computations are needed
to process the data resulting in a faster run time. In this paper, we propose a
way to further decrease the time required to use PCA by investigating how many
principal components may be omitted with minimal impact on the detection rate.
Using ACE to perform the detection, and then probability, and spectral fit for
identification, we find that the number of principal components can be reduced
by a substantial amount before seeing a noticeable change in detection rates.

实时高光谱检测中采用主成分分析进行降维可以加速处理时间，并且通过减少主成分数量可以在不显著降低检测率的情况下进一步减少处理时间。