图像滤波预处理不仅有助于增强图像信噪比和减少类间(intra-c lass)光谱可变性,而且还能够对影像中亮度均匀的区域进行平滑,从而为影像地物识别、分割和分类提供有力的支持。在Pope和Acton提出的两个多光谱图像各向异性扩散滤波模型的基础上,通过各向异性扩散和稳健统计学的联系,建立了基于B iwe ight Estim ator误差模型的扩散系数,同时利用非线性退化技术对梯度阈值的改进,提出了两个基于各向异性扩散方程的非线性滤波方法。提出的方法不仅能够有效地消除传感器随机噪声的影响,而且还能够很好地保持遥感图像上重要的细节边缘和影像质量。实验结果表明,不论是视觉效果还是质量统计分析,提出的扩散模型的性能优于Pope和Acton的各向异性扩散模型,是理想的多光谱图像保边缘滤波方法。

Image filtering preprocessing which is helpful for increasing the signal to noise ratio(SNR),decreasing the intra-class spectral variability and spatially smoothing homogeneous areas on the image can prove very useful for further discrimination of ground objects,image segmentation and classification processing.In this paper,two nonlinear anisotropic diffusion filtering methods are presented and they are based on the multispectral anisotropic diffusion models proposed by Pope and Acton.We build a couple of new diffusion coefficients in partial derivative equation(PDE) based on Tukey's biweight estimator error norm by recurring to the relationship between robust statistics and anisotropic diffusion incorporated with the nonlinear time-dependent cooling technique for gradient threshold.Our methods not only effectively remove the impulsive noise caused by sensors,but also preferably preserve important detailed edges and image quality in remotely sensed images.Experimental results are given to show that the improved methods have superiority capability over the multispectral anisotropic diffusion schemes proposed by Pope and Acton on visual judgment and quality statistical analysis and they are very ideal edge-preserving filtering methods.