Hyperspectral data rarely spans the full band space because of factors such as sensor noise, numerical round-off, sparse sampling, and band correlation introduced by data processing. Standard exploitation of data, which often does not consider the possibility of a reduced band space, leads to reduced detection performance. Spectral signature detection performance can be improved by estimating the covariance on a subset of the band space components. The decision about how to limit the band space can be determined by factors such as in-scene estimation of noise. In-scene estimation of noise can be used to optimize spectral signature detection when spectral filtering methods based on covariance inverses are used. We present here a method for determining instrument noise and a new method of covariance inverse regularization which increases spectral filtering performance.