Future multispectral and hyperspectral remote sensing systems and image archives will benefit from effective, high-fidelity image compression techniques. In evaluating the effects of compression upon the data, one must not only consider the qualitative and quantitative effects upon the images themselves, but also upon the end user products that are derived from the imagery through the application of environmental retrieval algorithms. At The Aerospace Corporation, we have developed a fast algorithm for image compression techniques known as the modulated lapped transform (MLT). This compression algorithm obviates many of the artifacts that are introduced by some of the standard compression techniques. One example of compression artifacting is the blocking errors from discrete cosine transformation (DCT) based algorithms, which include the JPEG compression scheme. The Aerospace MLT technique is a hybrid of the wavelet and DCT techniques. It employs our patented split-radix approach, which is the fastest DCT algorithm known today. In this paper, we compare Aerospace MLT to JPEG, using cloud imagery and Earth surface scene classification. We also discuss the availability of a cost- effective VLSI hardware implementation of the Aerospace compression algorithm. The modulated lapped transform employs a peano scan with a split-radix approach to avoid blockiness artifacts. It has excellent resistance to errors, and it is amenable to fast processing using a 1-D hardware architecture to process a 2-D image. This technique encapsulates the favorable aspects of the wavelet transforms and produces images which, when compressed 10:1 and decompressed, compare very favorably (using error statistics, classification accuracy and visual quality metrics) to the original uncompressed image.