1. A method for selecting a subset of hyperspectral imaging wavebands to detect a target in a scene, the method comprising: in a filtering engine provided with a plurality of spectra of a target and a covariance matrix of a scene, for each spectrum in the plurality of spectra of the target: computing a signal-to-clutter ratio of a full set of wavebands representing the probability of detecting the target viewed in the scene; selecting a subset of wavebands from the full set of wavebands; computing a signal-to-clutter ratio of the selected subset of wavebands representing the probability of detecting the target viewed in the scene using the selected subset of wavebands; comparing the signal-to-clutter ratio of the subset of wavebands to the signal-to-clutter ratio of the full set of wavebands; determining whether the subset of wavebands is a good enough set of wavebands based on the comparison; and providing the good enough set of wavebands to detect the target in the scene: wherein selecting the subset of wavebands includes determining, in a number of iterations, reduced sets of wavebands by: computing a matched filter weight for each waveband in an input set of wavebands, the input set of wavebands of a first iteration being the full set of wavebands and the input set of wavebands of subsequent iterations being the reduced sets of wavebands from previous iterations; deleting a waveband with the smallest matched filter weight from the input set of wavebands resulting in a reduced set of wavebands; and ending the iterations when the reduced set of wavebands has a specified number of wavebands, specified signal-to-clutter ratio or has a specified fraction of the total signal-to-clutter ratio for the full set of wavebands, the reduced set of wavebands being the subset of wavebands that is selected and from which the good enough set of wavebands is determined.