I am creating a signature file for a supervised classification for a subset of a LANDSAT image. I know what classes I would like to create, and have identified ideal areas for training. However, I am running into a problem in some of these areas in which the covariance matrix cannot be inverted, thus I am not able to run a parametric classification, such as Maximum Likelihood. I understand that the covariance matrix must be linear for this to happen and is a function of the pixels themselves and I cannot edit this. I am using layer-stacked LANDSAT 8 imagery with 14 bands (7 from spring imagery, 7 from winter imagery to detect leaf-on and leaf-off deciduous areas).
Is there a way to determine areas that would lend themselves to fit into a linear covariance matrix? I ran across literature that suggests that an increase in bands makes it more difficult to achieve this linear satisfaction for covariance matrix inversion. I am using ERDAS 2014. It has the option to run a non-parametric (such as parallelepiped) along with a parametric (max likelihood), but I would like to stick to maximum likelihood. Would larger training areas fix this? Or more smaller areas merged together? It seems that when adding new training areas it is random as to whether the cov matrix will be invertible or not. Thanks in advance!