局部均值与局部标准差法是目前应用最广泛的遥感图像噪声评估方法之一。该方法利用了局部均值与局部标准差的概念,对含有加性噪声的遥感图像进行噪声评估。但该方法受地物覆盖类型影响很大,当遥感图像中地物覆盖复杂时,会得到异常的噪声估算结果。产生这一现象的主要原因是遥感图像中包含边缘和纹理的不均匀子块。为降低地物覆盖复杂性对算法的影响,本文提出了基于边缘块剔出的局部均值与局部标准差法和基于高斯波形提取的局部均值与局部标准差法,前一种方法是削弱图像中包含边缘的不均匀子块的影响,后一种方法是提取反映均匀子块数量特征的高斯波形。利用同一次航空试验中获取的两幅AVIRIS辐射图像对改进后的算法进行了检验,结果表明改进后算法的健壮性明显提高,且噪声估算结果更准确。

Local Means and Local Standard Deviations method(LMLSD) is one of the most widely used methods for estimating the noise in remote sensing images.It employs local means and local standard deviations of small imaging blocks and can be used to evaluate images with additive noises.But this method is badly affected by land cover types,when the land cover types of the images vary greatly,estimation of noise can be invalid due to the effects of the heterogeneous blocks containing edges and texture features.In order to reduce the sensibility of LMLSD to land cover complexity,two improved methods were designed,one is to reduce the affection of the heterogeneous blocks,and the other is to pick up the Gaussian distribution that shows the characteristics of the heterogeneous blocks.We validated the two methods with two AVIRIS radiance images acquired in the same aerial experiment.In the validation the improved methods show distinctly enhanced robustness compared to the common LMLSD and the estimation of the noise is proved to be more accurate.