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We are trying to map land cover classes over a small watershed. We have selected training sites (during a field campaign in early 2017) and determined their spectral profiles on a Landsat 8 image acquired at the time of field surveying. In order to assess the land cover changes, we wanted to map the same cover classes at a previous year. Since our training sites might not be relevant, we wanted to perform supervised classification using endmembers spectra instead of ROIs. When importing those spectra inside ENVI's Endmember Collection toolbox, it appears that only Spectral Angle Mapper and Spectral Information Divergence classifiers could be used. Common algorithms such as Maximum Likelihood or Mahalanobis distance fail, returning the following error :

Problem: the selected algorithm requires that the collected endmember spectra all contain an associated covariance. ENVI is unable to continue because some of the endmembers collected to not have their covariance.

Could anyone help here ? Actually is that method relevant ? How can we possibly perform supervised classification using Maximum Likelihood/ Mahalanobis classifiers ?

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