The next generation of satellite-borne sensors will combine high spatial resolution with fine spectral resolution. A typical data set for a single frame of imagery may contain a few hundred images occupying many gigabytes of space. Clearly, traditional image processing algorithms cannot be directly applied to such a vast quantity of data. We investigate enhancement and compression algorithms that use the spectral correlation present in high-resolution imagery to reduce the computational complexity of processing the imagery. The algorithm employs a principal component transformation to reduce the size of the data set. Enhancing the reduced set of images provides equivalent results to processing each of the original images with far fewer computations. The compression algorithm utilizes a hybrid discrete cosine transform-differential pulse code modulation (DCT-DPCM) transform. The DCT is computed for each image, a bit map is generated for the DCT coefficients, and DPCM is used to encode the coefficients across the bands. Compression at less than 0.5 bits/pixel with negligible visual degradation is obtained.