摘要：

应用梯度变化检测遥感图像纹理边缘信息时存在过检、漏检、错检和弱抗噪性等问题。为此,结合分数阶微分差和高斯曲率滤波,提出一种边缘检测算法。通过分数阶微分差运算对全色遥感图像的梯度场进行非线性增强,利用高斯曲率滤波平滑图像非线性扩散部分,并寻找正则化能量最速下降点,优化微分过程中的分数阶次和迭代次数,改善有噪图像的边缘信息提取质量。实验结果表明,该算法可抑制遥感图像纹理边缘提取过程中噪声非线性放大和扩散产生的背景伪噪声,保留图像纹理边缘信息,具有较好的图像增强和边缘检测效果。

关键词: 边缘检测, 分数阶微分差, 高斯曲率滤波, 图像熵, 峰值信噪比

Abstract:

Gradient changes are used to detect edge information of remote sensing image texture which leads to over-detection,missed detection,wrong inspection and weak noise immunity.Therefore,an edge detection algorithm is proposed by combining fractional differential difference and Gauss curvature filtering.Fractional Differential Difference Operation(FDDO) is used to realize nonlinear enhancement of gradient fields of panchromatic remote sensing images.The Gaussian curvature is used to smooth the non-linear diffusion of the image to find the fastest descent point of the regularized energy.The number of fractional orders and iterations of the differential process are optimized to improve the quality of edge information of the noisy image.Experimental results show that the algorithm can suppress background pseudo-noise caused by non-linear amplification and diffusion of noise in the process of texture edge extraction of remote sensing images,and retain texture edge information.It has better image enhancement and edge detection effect.

Key words: edge detection, fractional differential difference, Gaussian curvature filtering, image entropy, Peak Signal to Noise Ratio(PSNR)

中图分类号: 

• TP391