We propose a subsampling method for robust estimation of regression models which is built on classical methods such as the least squares method. It makes use of the non-robust nature of the underlying classical method...We propose a subsampling method for robust estimation of regression models which is built on classical methods such as the least squares method. It makes use of the non-robust nature of the underlying classical method to find a good sample from regression data contaminated with outliers, and then applies the classical method to the good sample to produce robust estimates of the regression model parameters. The subsampling method is a computational method rooted in the bootstrap methodology which trades analytical treatment for intensive computation;it finds the good sample through repeated fitting of the regression model to many random subsamples of the contaminated data instead of through an analytical treatment of the outliers. The subsampling method can be applied to all regression models for which non-robust classical methods are available. In the present paper, we focus on the basic formulation and robustness property of the subsampling method that are valid for all regression models. We also discuss variations of the method and apply it to three examples involving three different regression models.展开更多
Rectification for airborne linear images is an indispensable preprocessing step. This paper presents in detail a two-step rectification algorithm. The first step is to establish the model of direct georeference positi...Rectification for airborne linear images is an indispensable preprocessing step. This paper presents in detail a two-step rectification algorithm. The first step is to establish the model of direct georeference position using the data provided by the Po- sitioning and Orientation System (POS) and obtain the mathematical relationships between the image points and ground reference points. The second step is to apply polynomial distortion model and Bilinear Interpolation to get the final precise rectified images. In this step, a reference image is required and some ground control points (GCPs) are selected. Experiments showed that the final rectified images are satisfactory, and that our two-step rectification algorithm is very effective.展开更多
传统图像去噪方法在去除声呐图像斑点噪声的同时,难以有效保留细节特征.针对该问题,提出一种基于密度聚类与灰度变换的非下采样剪切波域图像去噪方法.利用非下采样剪切波变换将含噪图像分解为高频系数和低频系数,根据声呐图像中斑点噪...传统图像去噪方法在去除声呐图像斑点噪声的同时,难以有效保留细节特征.针对该问题,提出一种基于密度聚类与灰度变换的非下采样剪切波域图像去噪方法.利用非下采样剪切波变换将含噪图像分解为高频系数和低频系数,根据声呐图像中斑点噪声的分布特性,采用基于密度的噪声应用空间聚类(Density-based Spatial Clustering of Applications with Noise,DBSCAN)算法对高频系数进行处理,分离噪声信号,保留细节信息;对低频系数进行灰度变换,以增强图像对比度.通过非下采样剪切波逆变换对处理后的高频系数和低频系数进行重构,实现图像去噪.实验结果表明,本文方法在改善图像均方误差、峰值信噪比和结构相似度等指标上效果明显,去噪后图像的视觉效果和边缘保持能力得到较大提升.随着噪声方差的逐渐增大,本文方法的优越性得到进一步体现,适用于具有高密度噪声的声呐图像去噪.展开更多
文摘We propose a subsampling method for robust estimation of regression models which is built on classical methods such as the least squares method. It makes use of the non-robust nature of the underlying classical method to find a good sample from regression data contaminated with outliers, and then applies the classical method to the good sample to produce robust estimates of the regression model parameters. The subsampling method is a computational method rooted in the bootstrap methodology which trades analytical treatment for intensive computation;it finds the good sample through repeated fitting of the regression model to many random subsamples of the contaminated data instead of through an analytical treatment of the outliers. The subsampling method can be applied to all regression models for which non-robust classical methods are available. In the present paper, we focus on the basic formulation and robustness property of the subsampling method that are valid for all regression models. We also discuss variations of the method and apply it to three examples involving three different regression models.
基金Project (No. 02DZ15001) supported by Shanghai Science and Technology Development Funds, China
文摘Rectification for airborne linear images is an indispensable preprocessing step. This paper presents in detail a two-step rectification algorithm. The first step is to establish the model of direct georeference position using the data provided by the Po- sitioning and Orientation System (POS) and obtain the mathematical relationships between the image points and ground reference points. The second step is to apply polynomial distortion model and Bilinear Interpolation to get the final precise rectified images. In this step, a reference image is required and some ground control points (GCPs) are selected. Experiments showed that the final rectified images are satisfactory, and that our two-step rectification algorithm is very effective.
文摘传统图像去噪方法在去除声呐图像斑点噪声的同时,难以有效保留细节特征.针对该问题,提出一种基于密度聚类与灰度变换的非下采样剪切波域图像去噪方法.利用非下采样剪切波变换将含噪图像分解为高频系数和低频系数,根据声呐图像中斑点噪声的分布特性,采用基于密度的噪声应用空间聚类(Density-based Spatial Clustering of Applications with Noise,DBSCAN)算法对高频系数进行处理,分离噪声信号,保留细节信息;对低频系数进行灰度变换,以增强图像对比度.通过非下采样剪切波逆变换对处理后的高频系数和低频系数进行重构,实现图像去噪.实验结果表明,本文方法在改善图像均方误差、峰值信噪比和结构相似度等指标上效果明显,去噪后图像的视觉效果和边缘保持能力得到较大提升.随着噪声方差的逐渐增大,本文方法的优越性得到进一步体现,适用于具有高密度噪声的声呐图像去噪.
