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Code-concatenation-based multiple classifer systems for automatic target recognition

In this paper, we propose a new multiple classifier system (MCS) based on two concatenated stages of multiple description coding models (MDC) and Support Vector Machine (SVM). This paper draws on concepts coming from a variety of disciplines that includes classical concatenated coding of error correcting codes, multiple description coding of wavelet based image compression, error correcting output codes (ECOC) of multiple classifier systems, and antithetic-common varaites of Monte Carlo Methods. In our previous work, we proposed and extended several methods in MDC to MCS with inspirations from two frameworks. First, we found that one of our methods is equivalent to one of the variance reduction techniques, called antithetic-common variates, in the Monte Carlo Methods (MCM). Second, another equivalent relation between one of our methods and transmitting data over heterogeneous network, especially wireless networks, are established. In this paper, we also include several support ideas. For example, preliminary surveys on the biological plausible of the MDC concepts are also included. One of the benefits of our approach is that it allows us to formulate a generalized class of signal processing based weak classification algorithms. This will be very applicable for MDC-SVM in high dimensional classification problems, such as image/target recognition. Performance results for automatic target recognition are presented for synthetic aperture radar (SAR) images from the MSTAR public release data set. From the experimental results, our proposed method outperform state-of-the-art multiple classifier systems, such as SVM-ECOC etc.