计算机工程

• 图形图像处理 • 上一篇    下一篇

极化SAR图像去相关目标检测改进算法

李艳玮,王雪瑞   

  1. (河南工程学院 计算机学院,郑州 451191)
  • 收稿日期:2015-09-29 出版日期:2016-09-15 发布日期:2016-09-15
  • 作者简介:李艳玮(1980-),女,讲师、硕士,主研方向为计算机智能、图像处理;王雪瑞,讲师、硕士。

Improved Algorithm of Decorrelated Target Detection for Polarimetric SAR Images

LI Yanwei,WANG Xuerui   

  1. (College of Computer,Henan Institute of Engineering,Zhengzhou 451191,China)
  • Received:2015-09-29 Online:2016-09-15 Published:2016-09-15

摘要: 针对极化合成孔径雷达(SAR)图像为多通道数据的特点,提出一种改进的极化SAR图像去相关目标检测算法。分析经典极化匹配滤波检测量在求取最优权值及后续统计分布推导中存在的问题,利用实测数据验证不同地物极化SAR数据通道之间的相关性。根据二维高斯分布的去相关理论,对极化匹配滤波检测量数据通道之间进行去相关,得到新的去相关极化匹配滤波检测量,该检测量在最优权值的求取上更具一般性。经过去相关处理后的极化匹配滤波检测量的各通道数据不存在相关性,且满足独立分布的复高斯随机变量,使后续的统计分布推导更为严格。实验结果表明,该算法能够有效区分目标与杂波,具有较高的检测率,且虚警目标点少。

关键词: 极化合成孔径雷达图像, 极化匹配滤波, 去相关, 目标检测, 二维高斯分布

Abstract: For the characterisitcs of multi-channel data of the polarimetric Synthetic Aperture Radar(SAR) image,an improved algorithm of polarimetric SAR image decorrelation target detection is proposed.The classical Polarimetric Matching Filter (PMF) metric is anaylzed in solving the optimal weight value and the subsequent derivation of the statistical distribution.The real polarimetric SAR data is used to validate the unignored correlations between the channels of the PMF metric for different surface features.The channels of the PMF metric are decorrelated according to the decorrelation theory for two-dimensional Gaussian distribution,thereby obtaining a new Decorrelated Polarimetric Matched Filter(DPMF) metric.The solution for the optimal weight of the DPMF metric is suitable for more general case compared with PMF metric.There is no correlation betwwen the channels of the DPMF metric after decorrelation and meets the independent distribution of complex Gauss random variables which makes the subsequent derivation of the statistical distribution more stringent.Experimental results for the real polarimetric SAR data show that the detection algorithm based on the DPMF metric can efficiently distinguish target and clutter with a high detection rate and less false alarm rate.

Key words: polarimetric Snthetic Apperture Radar(SAR) image, polarimetric matched filtering, decorrelation, target detection, two-dimensional Gaussian distribution

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