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。
The multiplicative nature of the speckle noise in SAR images is a big problem in SAR image segmentation.
乘法性质的SAR图像斑点噪声的SAR图像分割是一个大问题。
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。
Utilizing MRF (Markov Random Field) model to introduce the pixel's local context information, a quite accurate segmentation of SAR target chip image is realized.
文中通过利用马尔可夫随机场模型，引入图像象素的局部结构信息，有效实现了SAR目标切片图像的高精度分割。
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。
Aimed at SAR image interpretation, SVM shows good performance in image filtering, image segmentation, target discrimination and classification, as well as polarimetric SAR data classification.
针对SAR图像解译，SVM在图像滤波、图像分割、目标识别与分类、极化数据分类等过程中有很好的处理能力。
Two segmentation methods of SAR image are proposed.
给出了两种SAR图像分割方法。
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。
An unsupervised segmentation of SAR imagery based on Multiscale image block is proposed.
提出了一种基于多尺度图像块的SAR图像无监督分割方法。
A combined method based on Markov Random Field (MRF) model and morphological operation was presented for the segmentation of the SAR image in target monitoring.
针对目标监测分析中的SAR图像分割问题，构造了一种基于马尔可夫随机场(MRF)模型和形态学运算的处理方法。
Compared with other traditional segmentation methods, this method considers the global information and polarized characteristics of POL-SAR image adequately so as to get the accurate segmentation.
该方法与传统的分割方法相比，能够充分考虑极化SAR图像的全局信息和极化特征对图像进行精确的分割。
From SAR-images of island against single background, an image matching method based on image segmentation is proposed. Five processes were used in the algorithm: a.
针对单一背景的海岛合成孔径雷达（SAR）图像，提出一种基于图像分割的图像匹配方法。
Third, the initial segmentation is refined scale by scale to get the final segmentation of the SAR image based on the posterior probability of classification which is estimated by the mixture model.
最后根据SAR图像的统计性质，利用基于混合模型估计的分类后验概率将初始分割结果逐尺度进行细化得到SAR图像的最终分割。
Third, the initial segmentation is refined scale by scale to get the final segmentation of the SAR image based on the posterior probability of classification which is estimated by the mixture model.
最后根据SAR图像的统计性质，利用基于混合模型估计的分类后验概率将初始分割结果逐尺度进行细化得到SAR图像的最终分割。
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