The disclosure provides a filtering engine for selecting sparse filter components used to detect a material of interest (or specific target) in a hyperspectral imaging scene and applying the sparse filter to a plurality of pixels in the scene. The filtering engine transforms a spectral reference representing the material of interest to principal components space using the eigenvectors of the scene. It then ranks sparse filter components based on each transformed component of the spectral reference. The filtering engine selects sparse filter components based on their ranks. The filtering engine performs the subset selection quickly because the computations are minimized; it processes only the spectral reference vector and covariance matrix of the scene to do the subset selection rather than process a plurality of pixels in the scene, as is typically done. The spectral filter scores for the plurality of pixels are calculated efficiently using the sparse filter.