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A previous study adapted a variety of techniques derived from multi-spectral image classification to find objects amid cluttered backgrounds in hyperspectral imagery. That study quantitatively compared the adapted algorithms against a standard object search, the matched filter (MF) and a recently developed object detector, Adaptive Cosine Estimator (ACE) and found substantial reduction in false alarm rates for a given target detection probability. One adapted object search, Regularized Maximum Likelihood Classifier (RMLC), requires calculating the covariance matrix involving the average object spectral signature and the target pixels. The object covariance matrix requires a relatively large number of pixels to generate a non-singular, accurate covariance matrix. This study examines the robustness of the RMLC algorithm on number of training pixels, the optimal mixing covariance matrices, and choice of object subspaces for the ACE algorithm. The tests were applied to visible/near IR data collected from forest and desert environments. This study finds that high detection performance standards for RMLC are invariant for pixel number for homogenous targets, down to two pixels. Regularization is relatively unaffected by the choice of areas to optimize the object covariance matrix although targets that mix background appear to be more sensitive to choice of covariance matrix. Reducing the object subspace dimensions by using the average target signature or choosing the first principle component enhances ACE performance relative to using the entire object space.