NDLI: Hidden Markov modelling for SAR automatic target recognition

Content Provider	IEEE Xplore Digital Library
Author	Nilubol, C. Pham, Q.H. Mersereau, R.M. Smith, M.J.T. Clements, M.A.
Copyright Year	1998
Description	Author affiliation: Center for Signal & Image Process., Georgia Inst. of Technol., Atlanta, GA, USA (Nilubol, C.)
Abstract	This paper discusses the application of hidden Markov models (HMMs) to solve the translational and rotational invariant automatic target recognition (TRIATR) problem associated with SAR imagery. This approach is based on a cascade of these stages: preprocessing, feature extraction and selection, and classification. Preprocessing and feature extraction and selection involve successive applications of extraction operations from measurements of the Radon transform of target chips. The features which are invariant to changes in rotation, position and shifts, although not to changes in scale are optimized through the use of feature selection techniques. The classification stage successively takes as its inputs the multidimensional multiple observation sequences, parameterizes them statistically using continuous density models to capture target and background appearance variability, and thus results in the TRIATR-HMMs. Experimental results have demonstrated that the recognition rate is as high as 99% over both the training set and the testing set.
Starting Page	1061
Ending Page	1064
File Size	469989
Page Count	4
File Format	PDF
ISBN	0780344286
ISSN	15206149
DOI	10.1109/ICASSP.1998.675451
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-05-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Target recognition Discrete Fourier transforms Speech recognition Signal processing Image processing Application software Feature extraction Semiconductor device measurement Multidimensional systems
Content Type	Text
Resource Type	Article

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