Compression of sensor data is important for transmission and storage of digital infrared and SAR images. For speed and economy, one would like to achieve the highest compression ratios possible while preserving the critical information in the images, i.e., target information. Conventional compression methods such as JPEG, subband coding, fractal coding methods, and the like are tailored to optimizing the reconstructed output to achieve the most subjectively pleasing images possible. Their goal is to make the reconstructed images look as close to the original as possible. In the defense industry ATR paradigm, this is not the relevant optimality criterion. Rather it is preservation of target detection and recognition performance, a concept which is somewhat new in the compression community. In this paper we report on a compression strategy based on subband coding and vector quantization that can achieve compression ratios in excess of 250 to 1, while maintaining high levels of detection/recognition accuracy.