1. A method for selecting a subset of hyperspectral imaging scene spectral covariance matrix principal components to detect a material of interest or specific target in a scene, the method comprising: in a filtering engine provided with a set of whitened principal component coefficients of a spectral reference vector, the spectral reference vector representative of a material of interest or specific target: computing a signal-to-clutter ratio (SCR) of the spectral reference vector based on the set of whitened principal component coefficients of the spectral reference vector; ranking the contribution of each whitened principal component coefficient to the total SCR; selecting, based on the ranking, a subset of whitened principal component coefficients from the set of whitened principal component coefficients of the spectral reference vector; for each of a plurality of scene pixels in the scene, computing a sparse detection filter score for a subject scene pixel based on the selected subset of whitened principal component coefficients of the spectral reference vector and a respective subset of whitened principal component coefficients of the subject scene pixel having the same indices; and determining, based on the sparse detection filter score, whether the material of interest or specific target is present in the subject scene pixel.