Integrated approach to bandwidth reduction and mine detection in shallow water with reduced-dimension image compression and automatic target recognition algorithms

Frances B. Shin; David H. Kil; Gerald J. Dobeck

doi:10.1117/12.280853

22 July 1997 Integrated approach to bandwidth reduction and mine detection in shallow water with reduced-dimension image compression and automatic target recognition algorithms

Frances B. Shin, David H. Kil, Gerald J. Dobeck

Author Affiliations +

Proceedings Volume 3079, Detection and Remediation Technologies for Mines and Minelike Targets II; (1997) https://doi.org/10.1117/12.280853
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

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 P_D and P_FA. 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.

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Frances B. Shin, David H. Kil, and Gerald J. Dobeck "Integrated approach to bandwidth reduction and mine detection in shallow water with reduced-dimension image compression and automatic target recognition algorithms", Proc. SPIE 3079, Detection and Remediation Technologies for Mines and Minelike Targets II, (22 July 1997); https://doi.org/10.1117/12.280853

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