刘梦玲, 陈嘉宇, 孙洪. Joint Boost特征选择的SAR信息可视化技术研究[J]. 武汉大学学报 ( 信息科学版), 2012, 37(10): 1240-1243.
引用本文: 刘梦玲, 陈嘉宇, 孙洪. Joint Boost特征选择的SAR信息可视化技术研究[J]. 武汉大学学报 ( 信息科学版), 2012, 37(10): 1240-1243.
LIU Mengling, CHEN Jiayu, SUN Hong. ASAR Information Visualization Framework Based Joint Boost Feature Selection Method[J]. Geomatics and Information Science of Wuhan University, 2012, 37(10): 1240-1243.
Citation: LIU Mengling, CHEN Jiayu, SUN Hong. ASAR Information Visualization Framework Based Joint Boost Feature Selection Method[J]. Geomatics and Information Science of Wuhan University, 2012, 37(10): 1240-1243.

Joint Boost特征选择的SAR信息可视化技术研究

ASAR Information Visualization Framework Based Joint Boost Feature Selection Method

  • 摘要: 提出了一种基于Joint Boost特征选择的合成孔径雷达(synthetic aperture radar,SAR)信息可视化方法。实验选用了ESAR的德国某机场的极化干涉SAR数据,提取几乎所有极化干涉信息分量构成较为完备的特征信息集合,实验结果证明了该方法的有效性。

     

    Abstract: In order to using the invisible information in SAR images,a SAR information visualization framework based Joint Boost feature selection method for SAR images is proposed in this paper.This method utilizes a learning method which called Joint Boost to choose the underlying information.Then the underlying information has been input into YCbCr or RGB space to show the information.The experiments are carried on a Pol-InSAR image of German airport from ESAR and have extracted almost all the Pol-InSAR parameters to form an almost complete information set.The results reveal the proposed algorithm's efficient performances and superiorities.

     

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