Constrained Energy Minimization (CEM) is similar to Matched Filtering (MF) in that the only required knowledge is the target spectra to be detected. Using a specific constraint, CEM uses a finite impulse response (FIR) filter to pass through the desired target while minimizing its output energy resulting from a background other than the desired targets. A correlation or covariance matrix is used to characterize the composite unknown background. In a mathematical sense, MF is a mean-centered version of CEM, where the data mean is subtracted from all pixel vectors.

  1. From the Toolbox, select Classification > Supervised Classification > Constrained Energy Minimization Classification. The Contrained Energy Minimization Input File dialog appears.
  2. Select the input file and perform optional spatial and spectral subsetting, then click OK. The Endmember Collection:CEM dialog appears.
  3. Import spectra to match. For details, see Endmember Options and Manage Endmember Spectra.
  4. Click Apply. The Constrained Energy Minimization Parameters dialog appears.
  5. Use the toggle button to select Compute New Covariance Stats and enter an output statistics filename, or toggle to Use Existing Stats File.
  6. If you selected Compute New Covariance Stats: To remove anomalous pixels before calculating background statistics, enable the Subspace Background check box. Then, specify in the Background Threshold field the fraction of the background in the anomalous image to use for calculating the subspace background statistics. The threshold range is 0.500 to 1.000 (the entire image).
  7. Click the toggle button to select the Covariance Matrix or Correlation Matrix method for the calculation.
  8. Select output to File or Memory.
  9. Click OK.
  10. If you selected Use Existing Stats File, select the statistics file that corresponds to the input data file when the Input File dialog appears. This statistics file must contain both the mean and covariance statistics for the input data.

Constrained Energy Minimization Results

The results of CEM appear as a series of gray scale images, one for each selected endmember.

The default stretch setting provides good visibility for small features. If needed, you can apply a different stretch so that larger features in the image are visible.

Note: You can set a default stretch range so that you do not have to stretch the data each time they are displayed.