Model-based classification of radar images

被引:129
作者
Chiang, HC [1 ]
Moses, RL [1 ]
Potter, LC [1 ]
机构
[1] Ohio State Univ, Dept Elect Engn, Columbus, OH 43210 USA
关键词
model-based classification; parametric modeling; point correspondence; radar image analysis;
D O I
10.1109/18.857795
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A Bayesian approach is presented for model-based classification of images with application to synthetic-aperture radar Posterior probabilities are computed for candidate hypotheses using physical features estimated from sensor data along with features predicted from these hypotheses. The likelihood scoring allows propagation of uncertainty arising in both the sensor data and object models. The Bayesian classification, including the determination of a correspondence between unordered random features, is shown to be tractable, yielding a classification algorithm, a method for estimating error rates, and a tool for evaluating performance sensitivity, The radar image features used for classification are point locations with an associated vector of physical attributes; the attributed features are adopted from a parametric model of high-frequency radar scattering. With the emergence of wideband sensor technology, these physical features expand interpretation of radar imagery to access the frequency- and aspect-dependent scattering information carried in the image phase.
引用
收藏
页码:1842 / 1854
页数:13
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