Unsupervised Classification of Polarimetric SAR Images by EM Algorithm(Sensing)
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概要
- 論文の詳細を見る
In this paper, the expectation maximization (EM) algorithm is used for unsupervised classification of polarimetric synthetic aperture radar (SAR) images. The EM algorithm provides an estimate of the parameters of the underlying probability distribution functions (pdf's) for each class. The feature vector is 9-dimensional, consisting of the six magnitudes and three angles of the elements of a coherency matrix. Each of the elements of the feature vector is assigned a specific parametric pdf. In this work, all the features are supposed to be statistically independent. Then we present a two-stage unsupervised clustering procedure. The EM algorithm is first run for a few iterations to obtain an initial partition of, for example, four clusters. A randomly selected sample of, for example, 2% pixels of the polarimetric SAR image may be used for unsupervised training. In the second stage, the EM algorithm may be run again to reclassify the first stage clusters into smaller sub-clusters. Each cluster from the first stage will be processed separately in the second stage. This approach makes further classification possible as shown in the results. The training cost is also reduced as the number of feature vector in a specific cluster is much smaller than the whole image.
- 社団法人電子情報通信学会の論文
- 2007-12-01
著者
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Yang Jian
Department of Electronic Engineering, Tsinghua University
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Yang Jian
Tsinghua Univ. Beijing Chn
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Khan Kamran-ullah
Department Of Electronic Engineering Tsinghua University
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Yang Jian
Department Of Cardiology Renmin Hospital Of Wuhan University
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Zhang Weijie
Tsinghua Univ. Beijing Chn
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ZHANG Weijie
Department of Electronic Engineering, Tsinghua University
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Yang Jian
Department Of Cardiology Daping Hospital The Third Military Medical University
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Yang Jian
Department Of Anesthesiology University Of Florida College Of Medicine
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