基于梯度比率的SAR图像局部特征提取方法研究

Research on local feature extraction of SAR images based on gradient ratio

本文研究了基于像素灰度差值计算的LBP算子和基于梯度比率的LGRP算子等局部二值模式。首先介绍了基本LBP算子和其他几种LBP算子的变形模式,并通过光学图像和实测SAR图像对LBP算子进行性能评估。针对LBP对SAR图像乘性噪声敏感的问题,利用梯度比率计算的LGRP算子,并结合旋转不变LBP的抗旋转性,本文提出了一种改进的SAR图像LGRP特征,获得了对SAR图像的抗噪性和抗旋转性能。实验结果表明,由本文方法提取的SAR图像局部特征具有较好的不变性,可用于姿态角变化下的目标识别与图像纹理切片匹配。

In this study,we investigate a local binary pattern( LBP) operator based on a difference calculation and a local gradient ratio pattern( LGRP) operator based on a gradient ratio. First,we introduce a basic and several other LBP operators and evaluate the performance of the LBP operators using optical image and synthetic aperture radar( SAR) image analysis. To address the problem of LBP 's sensitivity to multiplicative noise in SAR images,we use the LGRP calculator based on the gradient ratio,combined with the anti-rotation characteristics of a rotationinvariant LBP,and propose an improved rotation-invariant LGRP characteristic for SAR images. Our experimental results demonstrate that the proposed feature has good invariant performance in target recognition and image texture slice matching with changes in the angle of attitude.