Two segmentation methods of SAR image are proposed.
给出了两种SAR图像分割方法。
Speckle is one of the most important characters of SAR image.
相干斑点噪声是SAR影像的重要特征之一。
SAR image edge withdrawing is a important content of image proceesion.
SAR图像的边缘提取是其图像处理的一项重要内容。
Methods of reducing speckle noise in SAR image are discussed in this paper.
探讨了抑制合成孔径雷达图像相干斑噪声的方法。
The size of filtering window has obvious impact on the effect of SAR image filtering.
滤波窗口大小的选择直接影响SAR图像滤波的效果。
The extracted line graph can be used for SAR image vectorization and comprehension etc.
提取的直线图可以用于遥感图像矢量化、自动目标识别等方面。
They also make SAR image processing and research become a hot topic in signal processing.
因此SAR图像的处理和研究成为当前信号处理领域的一个热点。
With the experiment result, the method is proved an efficient one of SAR image segmentation.
实验结果表明,该方法是一种有效的SAR图像分割方法。
And also make SAR image processing and research become a hot topic in signal processing domain.
也使SAR图像的处理和研究成为当前信号处理领域的一个热点。
In this paper, SAR image speckle suppression is analyzed from the view of mathematical physics.
本文首先以数学物理的观点描述了SAR图像斑点噪声抑制问题。
Containing speckles, an SAR image, can not be classified well by using the traditional methods.
SAR图像包含有相干斑噪声,传统的方法不能很好地对SAR图像进行分类。
The description and extraction of SAR image texture feature is important to texture segmentation.
SAR图像纹理特征的描述和提取是纹理分割的关键。
The paper presents an algorithm of automatic SAR image segmentation based on minimum error ratio.
文章提出了一种基于最小错误率的SAR图象自动分割算法。
Besides, many classification errors are caused by mixed pixels and speckle noise of the SAR image.
另外,许多分类错误是由SAR图像的像素点类别混淆和相干斑噪声干扰引起的。
Synthetic Aperture Radar (SAR) image classification is a key technique of SAR image interpretation.
合成孔径雷达(SAR)图像分类是SAR图像解译的关键技术之一。
The experimental result proves that the proposed method is effective in edge detection of SAR image.
实验结果表明该方法是一种有效的SAR图像边缘检测方法。
This parameter can be employed for ship detection and bridge detection from a polarimetric SAR image.
该参数能在河流区域很好地进行舰船检测与桥梁检测。
The segmentation of SAR target chip image is an important process for target recognition based on SAR image.
SAR目标切片图像分割是基于SAR图像目标识别的一个重要步骤。
This algorithm can protect edge-characteristics of SAR image, along with a very good speckle reduction effort.
本算法能够完全保留SAR图像边缘特征,同时对相干斑具有较好的抑制能力。
This paper presents an algorithm about SAR image change detection based on Principal Component Analysis (PCA).
该文提出一种基于主分量分析(pca)的SAR图像变化检测算法。
The test indicates that the method is better to been applied in extracting roads in full-polarimetric SAR image.
实验表明:该方法应用到全极化sar影像中的道路的提取中效果较好。
The analysis of the statistic characteristics of SAR image is essential for the SAR image analysis and processing.
对SAR图像统计特性的分析是进行SAR图像处理与分析的基础。
But the distortion of SAR image brought by elevation is very difficult to be corrected by polynomial rectification.
但是,因高差引起的变形很难通过一般的多项式纠正方法进行改正。
This paper focuses on the research of speckle filtering algorithms of SAR image and obtains some useful conclusions.
本文以机载合成孔径雷达图像相干斑滤波算法为研究内容,得出了一些有益的结论。
To solve the problem of losing part of edge in the SAR image de-noising, the edge of image can be preserves beforehand.
而常用的去噪算法在去噪的同时会损失图像的边缘信息,所以本文在去噪前预先保留了图像的边缘信息。
In this paper, a kind of SAR image segmentation method based on the criterion of likelihood difference function is proposed.
这一部分我们给出了本文中竞争风险混合模型的描述及分组数据下的似然函数。
According to the Range Doppler equations, the geometric calibration of SAR image can be realized using SAR imaging parameters.
依据合成孔径雷达(SAR)距离方程和多普勒方程,用SAR成像参数可实现SAR图像的几何校正。
Two step algorithm is proposed for unsupervised detection of linear structure from SAR image, in particular, the road network detection.
提出一种两步算法用于从合成孔径雷达(SAR)图像中无监督地提取线性特征,特别是公路网的提取。
Automatic ship detection is one of the important methods that make ship sailing safely. SAR image can be well applied to detect the ship.
船只检测是实现船只航行安全的重要方法之一,利用SAR图像可实现船只检测。
SAR image nonlinear iterative filtering approach based on correlated neighborhood model is presented, it can restrain error accumulation.
提出一种基于相关邻域模型可抑制误差积累的SAR图像非线性迭代滤波方法。
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