The performances of discrete-cosine-transform (DCT) JPEG and wavelet-transform (WT) JPEG-2000 for the Karhunen-Loeve-Transform (KLT) based lossy compression of multispectral imagery data are evaluated and compared. The evaluation is based on the measured amount of compression-induced root mean square error in the reconstructed imagery and, more importantly, the impact of compression on the classification of imagery data. We have opted to use classification to assess the impact on compression since it is one of the most widely used forms of machine exploitation procedures. An unsupervised classification via a thematic map is implemented. It is assumed that results for a supervised classification would be similar. The impact of compression is examined at various compression ratios for data obtained from two sensor platforms, LANDSAT TM satellite test imagery with a 30m footprint, and ERIM M7 Sensor aerial test imagery with a 4-6m footprint. Preliminary results, based on the selected test imagery and the selected multispectral bandwidth compression scheme, indicate that the JPEG 2000 generally outperforms the baseline JPEG by a small margin. The results are based on the root-mean-square (RMS) error and the classification accuracy and pertain to imagery with less than 50m footprints. For the 4-6m-footprint ERIM aerial test imagery, JPEG 2000 produces up to four percent higher classification accuracy while incurring up to twelve percent smaller RMS error. However, for the 30m-footprint LANDSAT test imagery, the performance of JPEG and JPEG 2000 are nearly the same. This study does not include imagery with greater than 50m footprint, e.g., NOAA's AVHRR with 1.1 km footprint. For this type of imagery, classification should be performed via a spectral unmixing procedure, instead of a thematic map, since the pixels do not represent pure species.