In distributed underwater signal processing for area surveillance and sanitization during regional conflicts, it is often necessary to transmit raw imagery data to a remote processing station for detection-report confirmation and more sophisticated automatic target recognition (ATR) processing. Because of he limited bandwidth available for transmission, image compression is of paramount importance. At the same time, preservation of useful information that contains essential signal attributes is crucial for effective mine detection and classification in shallow water. In this paper, we present an integrated processing strategy that combines image compression and ATR algorithms for superior detection performance while achieving maximal bandwidth reduction. Our reduced-dimension image compression algorithm comprises image-content classification for the subimage-specific transformation, principal component analysis for further dimension reduction, and vector quantization to obtain minimal information state. Next, using an integrated pattern recognition paradigm, our ATR algorithm optimally combines low-dimensional features and an appropriate classifier topology to extract maximum recognition performance from reconstructed images. Instead of assessing performance of the image compression algorithm in terms of commonly used peak signal-to-noise ratio or normalized mean-squared error criteria, we quantify our algorithm performance using a metric that reflects human and operational factors - ATR performance. Our preliminary analysis based on high-frequency sonar real data indicates that we can achieve a compression ratio of up to 57:1 with minimal sacrifice in PD and PFA. Furthermore, we discuss the concept of the classification Cramer-Rao bound in terms of data compression, sufficient statistics, and class separability to quantify the extent to which a classifier approximates the Bayes classifier.