We present a bandwidth compression scheme suitable for transmission of radiometric data collected bytoday's sensitive and high-resolution sensors. Specific design constraints associated with this application are requirements for (1) near-lossless coding, (2) handling of a high dynamic range, and (3) placement of an upper bound on maximum coding error, as opposed to the average or rms coding error. In this approach both the spectral and spatial correlations in the data are exploited to reduce its bandwidth. Spectral correlation is first removed via the Karhunen-Loève (KL) transformation. An adaptive discrete cosine transform coding technique is then applied to the resulting spectrally decorrelated data. Because the actual coding is done in the transform domain, each individual coding error spreads over an entire block of data when reconstructed. This helps to reduce significantly the maximum error and, as such, makes this approach very suitable for this application. A useful by-product of this approach is that it readily provides some feature classification capability, such as cloud typing, through the interpretation of KL-transformed images. Since each KL-transformed image is a linear combination of all the spectral images, it represents a blend of information present in the entire spectral image set. As such, it could solely render some useful information not readily detectable from the ensemble of spectral images. This may be of particular utility for situations in which a photo interpreter may not have the time or the opportunity to inspect the entire set of images.