This paper describes progress on an Automatic Target Recognition (ATR) system for Synthetic Aperture Radar (SAR) imagery. The system is based upon a feature extraction, data ordering, and statistical modeling paradigm. Feature extraction is performed by applying image segmentation to convert the SAR imagery into one of four pixel classes. A description of a real-time image segmentation design is given. The segmented imagery is re-ordered from a two dimensional (2D) spatial representation to a sequential representation through the use of multiple Radon '&ansforms (RT). Finally, the re-ordered data is classified by target type by appling Hidden Markov Model (HMM) decoding techniques. Performance results on the MSTAR public targets database is provided.
In recent years, many image segmentation approaches have been based on Markov random fields (MRFs). The main assumption of the MRF approaches is that the class parameters are known or can be obtained from training data. In this paper the authors propose a novel method that relaxes this assumption and allows for simultaneous parameter estimation and vector image segmentation. The method is based on a tree structure (TS) algorithm which is combined with Besag's iterated conditional modes (ICM) procedure. The TS algorithm provides a mechanism for choosing initial cluster centers needed for initialization of the ICM. The authors' method has been tested on various one-dimensional (1-D) and multidimensional medical images and shows excellent performance. In this paper the authors also address the problem of cluster validation. They propose a new maximum a posteriori (MAP) criterion for determination of the number of classes and compare its performance to other approaches by computer simulations.
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