An enhancement to a previously developed Karhunen-Loeve/ discrete cosine transform-based multispectral bandwidth compression technique (Saghri et al., 1995) is presented. This enhancement is achieved via the addition of a spectral screening module prior to the spectral decorrelation process. The objective of the spectral screening module is to identify a set of unique spectral signatures in a block of multispectral data to be used in the subsequent spectral decorrelation module. The number of unique signatures found depends on the desired spectral angle separation, irrespective of their frequency of occurrence. This set of unique spectral signatures, instead of the signature of each and every point in the block of data, is used to construct the spectral covariance matrix and the resulting Karhunen-Loeve spectral transformation matrix that is used to spectrally decorrelate the multispectral images. The significance of this modification is that the covariance matrix so constructed is not entirely based on the statistical significance of the individual spectra in the block but rather on the uniqueness of the individual spectra. Without this added spectral screening feature, small objects and ground features would likely be manifested in the low eigen- planes mixed with all of the noise present in the scene. Since these lower eigenplanes are coded via the subsequent Joint Photographic Experts Group (JPEG) compression module at a much lower bit rate, the fidelity of these small objects is severely impacted by compression- induced error. However, the addition of the proposed spectral screening module relegates these small objects into the higher eigenplanes and hence greatly enhances the preservation of their fidelities in the compression process. This modification alleviates the need to update the covariance matrix frequently over small subblocks, resulting in a reduced overhead bit requirement and a much simpler implementation task.